Publications

2017

  • P. Arroba, J. M. Moya, J. L. Ayala, and R. Buyya, “Dynamic voltage and frequency scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers,” Concurrency and computation: practice and experience, vol. 29, iss. 10, p. e4067+, 2017. doi:10.1002/cpe.4067
    [BibTeX] [Download PDF]
    @article{AMA+17,
    author = {Arroba, Patricia and Moya, Jos\'{e} M. and Ayala, Jos\'{e} L. and Buyya, Rajkumar},
    citeulike-article-id = {14435515},
    citeulike-linkout-0 = {http://dx.doi.org/10.1002/cpe.4067},
    day = {25},
    doi = {10.1002/cpe.4067},
    issn = {15320626},
    journal = {Concurrency and Computation: Practice and Experience},
    keywords = {energy, greenlsi, josem},
    month = may,
    number = {10},
    pages = {e4067+},
    posted-at = {2017-09-21 15:09:26},
    priority = {2},
    title = {Dynamic Voltage and Frequency Scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers},
    url = {http://dx.doi.org/10.1002/cpe.4067},
    volume = {29},
    year = {2017}
    }

  • J. Pagán, R. Fallahzadeh, H. Ghasemzadeh, J. M. Moya, J. L. Risco-Martín, and J. L. Ayala, “An optimal approach for Low-Power migraine prediction models in the State-of-the-Art wireless monitoring devices,” in Design, automation and test in europe 2017 (date’17), 2017. doi:10.23919/DATE.2017.7927193
    [BibTeX] [Abstract] [Download PDF]

    Wearable monitoring devices for ubiquitous health care are becoming a reality that has to deal with the battery autonomy of the devices. Several research areas are focusing their efforts to reduce the energetic impact in these motes: from efficient micro-architectures, to on-node data processing techniques. In this paper we focus in the optimization of the energy consumption in monitorization devices in the prediction of symptomatic events in chronic diseases in real time. To do this, we have developed an optimization methodology that incorporates information of energy the consumption due to the execution of the process to calculate the predicted value in real time, and the energy consumption of the sensor used. These two objective functions joint to accuracy of the predictive model to find an optimal solution. As a result of the methodology we show that we are able to get a set of possible solutions and, according to an error tolerance, we are able to even improve the energy consumption of the computing process up to 90\% with a acceptable loss in the accuracy. The proposed optimization methodology can be applied to any prediction modeling scheme to introduce the concept of energy efficiency. In this work we test the framework using Grammatical Evolutionary algorithms in the prediction of chronic migraines.

    @inproceedings{PFG+17,
    abstract = {Wearable monitoring devices for ubiquitous health care are becoming a reality that has to deal with the battery autonomy of the devices. Several research areas are focusing their efforts to reduce the energetic impact in these motes: from efficient micro-architectures, to on-node data processing techniques. In this paper we focus in the optimization of the energy consumption in monitorization devices in the prediction of symptomatic events in chronic diseases in real time. To do this, we have developed an optimization methodology that incorporates information of energy the consumption due to the execution of the process to calculate the predicted value in real time, and the energy consumption of the sensor used. These two objective functions joint to accuracy of the predictive model to find an optimal solution. As a result of the methodology we show that we are able to get a set of possible solutions and, according to an error tolerance, we are able to even improve the energy consumption of the computing process up to 90\% with a acceptable loss in the accuracy. The proposed optimization methodology can be applied to any prediction modeling scheme to introduce the concept of energy efficiency. In this work we test the framework using Grammatical Evolutionary algorithms in the prediction of chronic migraines.},
    author = {Pag\'{a}n, Josu\'{e} and Fallahzadeh, Ramin and Ghasemzadeh, Hassan and Moya, Jos\'{e} M. and Risco-Mart\'{\i}n, Jos\'{e} L. and Ayala, Jos\'{e} L.},
    booktitle = {Design, Automation and Test in Europe 2017 (DATE'17)},
    citeulike-article-id = {14230234},
    citeulike-linkout-0 = {http://dx.doi.org/10.23919/DATE.2017.7927193},
    doi = {10.23919/DATE.2017.7927193},
    keywords = {aware, bio, biosignal, embedded, evolution, feature, grammatical, greenlsi, josem, optimization, power, prediction},
    location = {Laussane, Swizerland},
    month = mar,
    posted-at = {2017-09-21 15:05:20},
    priority = {2},
    title = {An Optimal Approach for {Low-Power} Migraine Prediction Models in the {State-of-the-Art} Wireless Monitoring Devices},
    url = {http://dx.doi.org/10.23919/DATE.2017.7927193},
    year = {2017}
    }

  • J. Pagán, J. M. Moya, S. Mittal, and J. L. Ayala, “Advanced migraine prediction simulation system,” in Summer simulation conference, 2017.
    [BibTeX] [Download PDF]
    @inproceedings{PMM+17,
    author = {Pag\'{a}n, Josu\'{e} and Moya, Jos\'{e} M. and Mittal, Saurabh and Ayala, Jos\'{e} L.},
    booktitle = {Summer Simulation Conference},
    citeulike-article-id = {14396408},
    citeulike-linkout-0 = {http://64581scsi.prod.omnipress.atex.cniweb.net/64581-scsia-1.3644509/t001-1.3644814/f004-1.3644889/a016-1.3644893/an016-1.3644894},
    day = {9},
    keywords = {bio, greendisc, greenlsi, josem},
    month = jul,
    organization = {SCS},
    posted-at = {2017-09-21 15:04:20},
    priority = {2},
    title = {Advanced Migraine Prediction Simulation System},
    url = {http://64581scsi.prod.omnipress.atex.cniweb.net/64581-scsia-1.3644509/t001-1.3644814/f004-1.3644889/a016-1.3644893/an016-1.3644894},
    year = {2017}
    }

  • I. Penas, M. Zapater, J. L. Risco-Martín, and J. L. Ayala, “SFIDE: a simulation infrastructure for data centers,” in Summer simulation conference, 2017.
    [BibTeX] [Download PDF]
    @inproceedings{PZR+17,
    author = {Penas, Ignacio and Zapater, Marina and Risco-Mart\'{\i}n, Jos\'{e} L. and Ayala, Jos\'{e} L.},
    booktitle = {Summer Simulation Conference},
    citeulike-article-id = {14396410},
    citeulike-linkout-0 = {http://64581scsi.prod.omnipress.atex.cniweb.net/64581-scsia-1.3644509/t001-1.3644814/f004-1.3644889/a017-1.3644890/an017-1.3644891},
    day = {9},
    keywords = {dc, greendisc, greenlsi},
    month = jul,
    organization = {SCS},
    posted-at = {2017-09-21 15:03:52},
    priority = {2},
    title = {{SFIDE}: A Simulation Infrastructure For Data Centers},
    url = {http://64581scsi.prod.omnipress.atex.cniweb.net/64581-scsia-1.3644509/t001-1.3644814/f004-1.3644889/a017-1.3644890/an017-1.3644891},
    year = {2017}
    }

  • M. Fuentes, J. Fraile-Ardanuy, J. L. Risco-Martín, and J. M. Moya, “Feasibility study of a Building-Integrated PV manager to power a Last-Mile electric vehicle sharing system,” International journal of photoenergy, vol. 2017, pp. 1-12, 2017. doi:10.1155/2017/8679183
    [BibTeX] [Abstract] [Download PDF]

    Transportation is one of the largest single sources of air pollution in urban areas. This paper analyzes a model of solar-powered vehicle sharing system using building-integrated photovoltaics ({BIPV}), resulting in a zero-emission and zero-energy mobility system for last-mile employee transportation. As a case study, an electric bicycle sharing system between a public transportation hub and a work center is modeled mathematically and optimized in order to minimize the number of pickup trips to satisfy the demand, while minimizing the total energy consumption of the system. The whole mobility system is fully powered with {BIPV}-generated energy. Results show a positive energy balance in e-bike batteries and pickup vehicle batteries in the worst day of the year regarding solar radiation. Even in this worst-case scenario, we achieve reuse rates of 3.8 people per bike, using actual data. The proposed system manages {PV} energy using only the batteries from the electric vehicles, without requiring supportive energy storage devices. Energy requirements and {PV} generation have been analyzed in detail to ensure the feasibility of this approach.

    @article{FFR+17,
    abstract = {Transportation is one of the largest single sources of air pollution in urban areas. This paper analyzes a model of solar-powered vehicle sharing system using building-integrated photovoltaics ({BIPV}), resulting in a zero-emission and zero-energy mobility system for last-mile employee transportation. As a case study, an electric bicycle sharing system between a public transportation hub and a work center is modeled mathematically and optimized in order to minimize the number of pickup trips to satisfy the demand, while minimizing the total energy consumption of the system. The whole mobility system is fully powered with {BIPV}-generated energy. Results show a positive energy balance in e-bike batteries and pickup vehicle batteries in the worst day of the year regarding solar radiation. Even in this worst-case scenario, we achieve reuse rates of 3.8 people per bike, using actual data. The proposed system manages {PV} energy using only the batteries from the electric vehicles, without requiring supportive energy storage devices. Energy requirements and {PV} generation have been analyzed in detail to ensure the feasibility of this approach.},
    author = {Fuentes, Manuel and Fraile-Ardanuy, Jes\'{u}s and Risco-Mart\'{\i}n, Jos\'{e} L. and Moya, Jos\'{e} M.},
    citeulike-article-id = {14435513},
    citeulike-linkout-0 = {http://dx.doi.org/10.1155/2017/8679183},
    doi = {10.1155/2017/8679183},
    issn = {1110-662X},
    journal = {International Journal of Photoenergy},
    keywords = {greenlsi},
    pages = {1--12},
    posted-at = {2017-09-21 14:57:26},
    priority = {2},
    title = {Feasibility Study of a {Building-Integrated} {PV} Manager to Power a {Last-Mile} Electric Vehicle Sharing System},
    url = {http://dx.doi.org/10.1155/2017/8679183},
    volume = {2017},
    year = {2017}
    }

  • T. M. Higuera-Toledano, J. L. Risco-Martin, P. Arroba, and J. L. Ayala, “Green adaptation of Real-Time web services for industrial CPS within a cloud environment,” Ieee transactions on industrial informatics, vol. 13, iss. 3, pp. 1249-1256, 2017. doi:10.1109/tii.2017.2693365
    [BibTeX] [Download PDF]
    @article{HRA+17,
    author = {Higuera-Toledano, M. Teresa and Risco-Martin, Jose L. and Arroba, Patricia and Ayala, Jose L.},
    citeulike-article-id = {14427500},
    citeulike-linkout-0 = {http://dx.doi.org/10.1109/tii.2017.2693365},
    doi = {10.1109/tii.2017.2693365},
    issn = {1551-3203},
    journal = {IEEE Transactions on Industrial Informatics},
    keywords = {energy, greenlsi},
    month = jun,
    number = {3},
    pages = {1249--1256},
    posted-at = {2017-09-21 14:53:37},
    priority = {2},
    title = {Green Adaptation of {Real-Time} Web Services for Industrial {CPS} Within a Cloud Environment},
    url = {http://dx.doi.org/10.1109/tii.2017.2693365},
    volume = {13},
    year = {2017}
    }

  • J. Pagán, M. Zapater, and J. L. Ayala, “Power transmission and workload balancing policies in eHealth mobile cloud computing scenarios,” Future generation computer systems, 2017. doi:10.1016/j.future.2017.02.015
    [BibTeX] [Abstract] [Download PDF]

    A real {IoT} {eHealth} scenario for and prediction of the migraine disease is shown. Energy efficiency techniques in every level of the motorization network. Low-power techniques in the radio and data processing in the sensing nodes. Workload balancing policies are carried out in coordinator nodes and Data Centers. The results show the energetic and economic benefits of the energy policies applied. The Internet of Things ({IoT}) holds big promises for healthcare, especially in proactive personal {eHealth}. Prediction of symptomatic crises in chronic diseases in the {IoT} scenario leads to the deployment of ambulatory monitoring systems. These systems place a major concern in the amount of data to be processed and the intelligent management of the energy consumption. The huge amount of data generated for these systems require high computing capabilities only available in Data Centers. This paper presents a real case of prediction in the {eHealth} scenario, devoted to neurological disorders. The presented case study focuses on the migraine headache, a disease that affects around 15\% of the European population. This paper extrapolates results from real data and simulations in a study where migraine patients are monitored using an unobtrusive Wireless Body Sensor Network. Low-power techniques are applied in monitorization nodes. Techniques such us: on-node signal processing and radio policies to make node’s autonomy longer and save energy, have been applied. Workload balancing policies are carried out in the coordinator nodes and Data Centers to reduce the computational burden in these facilities and minimize its energy consumption. Our results draw average savings of € 288 million in this {eHealth} scenario applied only to 2\% of European migraine sufferers; in addition to savings of € 1272 million due to the benefits of the migraine prediction.

    @article{PZA17,
    abstract = { A real {IoT} {eHealth} scenario for and prediction of the migraine disease is shown. Energy efficiency techniques in every level of the motorization network. Low-power techniques in the radio and data processing in the sensing nodes. Workload balancing policies are carried out in coordinator nodes and Data Centers. The results show the energetic and economic benefits of the energy policies applied. The Internet of Things ({IoT}) holds big promises for healthcare, especially in proactive personal {eHealth}. Prediction of symptomatic crises in chronic diseases in the {IoT} scenario leads to the deployment of ambulatory monitoring systems. These systems place a major concern in the amount of data to be processed and the intelligent management of the energy consumption. The huge amount of data generated for these systems require high computing capabilities only available in Data Centers. This paper presents a real case of prediction in the {eHealth} scenario, devoted to neurological disorders. The presented case study focuses on the migraine headache, a disease that affects around 15\% of the European population. This paper extrapolates results from real data and simulations in a study where migraine patients are monitored using an unobtrusive Wireless Body Sensor Network. Low-power techniques are applied in monitorization nodes. Techniques such us: on-node signal processing and radio policies to make node's autonomy longer and save energy, have been applied. Workload balancing policies are carried out in the coordinator nodes and Data Centers to reduce the computational burden in these facilities and minimize its energy consumption. Our results draw average savings of € 288 million in this {eHealth} scenario applied only to 2\% of European migraine sufferers; in addition to savings of € 1272 million due to the benefits of the migraine prediction.},
    author = {Pag\'{a}n, Josu\'{e} and Zapater, Marina and Ayala, Jos\'{e} L.},
    citeulike-article-id = {14277319},
    citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.future.2017.02.015},
    doi = {10.1016/j.future.2017.02.015},
    issn = {0167739X},
    journal = {Future Generation Computer Systems},
    keywords = {energy, greenlsi, migraine, optimization},
    month = feb,
    posted-at = {2017-09-21 14:51:05},
    priority = {2},
    title = {Power transmission and workload balancing policies in {eHealth} mobile cloud computing scenarios},
    url = {http://dx.doi.org/10.1016/j.future.2017.02.015},
    year = {2017}
    }

  • A. Corredera, M. Romero, and J. M. Moya, “Affective computing for smart operation: a survey and comparative analysis of the available tools, libraries and web services,” International journal of innovative and applied research (ijiar), vol. 5, iss. 9, pp. 12-35, 2017.
    [BibTeX] [Abstract] [Download PDF]

    In this paper, we make a deep search of the available tools in the market, at the current state of the art of Sentiment Analysis. Our aim is to optimize the human response in Datacenter Operations, using a combination of research tools, that allow us to decrease human error in general operations, managing Complex Infrastructures. The use of Sentiment Analysis tools is the first step for extending our capabilities for optimizing the human interface. Using different data collections from a variety of data sources, our research provides a very interesting outcome. In our final testing, we have found that the three main commercial platforms ({IBM} Watson, Google Cloud and Microsoft Azure) get the same accuracy (89-90\%). for the different datasets tested, based on Artificial Neural Network and Deep Learning techniques. The other stand-alone Applications or {APIs}, like Vader or {MeaninCloud}, get a similar accuracy level in some of the datasets, using a different approach, semantic Networks, such as Concepnet , but the model can easily be optimized above 90\% of accuracy, just adjusting some parameter of the semantic model. This paper points to future directions for optimizing {DataCenter} Operations Management and decreasing human error in complex environments.

    @article{CRM17,
    abstract = {In this paper, we make a deep search of the available tools in the market, at the current state of the art of Sentiment Analysis. Our aim is to optimize the human response in Datacenter Operations, using a combination of research tools, that allow us to decrease human error in general operations, managing Complex Infrastructures. The use of Sentiment Analysis tools is the first step for extending our capabilities for optimizing the human interface. Using different data collections from a variety of data sources, our research provides a very interesting outcome. In our final testing, we have found that the three main commercial platforms ({IBM} Watson, Google Cloud and Microsoft Azure) get the same accuracy (89-90\%). for the different datasets tested, based on Artificial Neural Network and Deep Learning techniques. The other stand-alone Applications or {APIs}, like Vader or {MeaninCloud}, get a similar accuracy level in some of the datasets, using a different approach, semantic Networks, such as Concepnet , but the model can easily be optimized above 90\% of accuracy, just adjusting some parameter of the semantic model. This paper points to future directions for optimizing {DataCenter} Operations Management and decreasing human error in complex environments.},
    author = {Corredera, Alberto and Romero, Marta and Moya, Jose M.},
    citeulike-article-id = {14435511},
    citeulike-linkout-0 = {http://www.journalijiar.com/article/652/affective-computing-for-smart-operations:-a-survey-and-comparative-analysis-of-the-available-tools,-libraries-and-web-services./},
    day = {21},
    issn = {2348-0319},
    journal = {International Journal of Innovative and Applied Research (IJIAR)},
    keywords = {cyberops, greenlsi, josem},
    month = sep,
    number = {9},
    pages = {12--35},
    posted-at = {2017-09-21 14:42:20},
    priority = {2},
    title = {Affective Computing for Smart Operation: A Survey and Comparative Analysis of the Available Tools, Libraries and Web Services},
    url = {http://www.journalijiar.com/article/652/affective-computing-for-smart-operations:-a-survey-and-comparative-analysis-of-the-available-tools,-libraries-and-web-services./},
    volume = {5},
    year = {2017}
    }

2016

  • J. L. Risco Martín, S. Mittal, J. C. Fabero, P. Malagón, and J. L. Ayala, “Real-time Hardware/Software co-design using devs-based transparent M&\#38;S framework,” in Proceedings of the summer computer simulation conference, San Diego, CA, USA, 2016.
    [BibTeX] [Abstract] [Download PDF]

    Design and development of hard {Real-Time} ({RT}) embedded systems present several crucial requirements regarding criticality and timeliness of these systems. Formal methods have been presented as a promising alternative to deal with the design issues of these applications. However, these formal method do not scale well in complex systems. Modeling and Simulation ({M&S}) provides cost-effective approaches to verify and validate the design and implementation details of complex {RT} applications. Nevertheless, {M&S} approaches and artifacts are usually discarded in the later phases of the development. Discrete Event Systems Specification ({DEVS}) provides an appropriate {M&S} framework to provide formal specifications to the actual {RT} system, incrementally moving from software specifications to a full hardware embedded system. In this work, we propose a hardware-in-the-loop model-driven method, based on {DEVS} for {RT}/embedded application/systems engineering. Our approach is based on an incremental substitution of {DEVS} virtual software models with Unix-compliant device files through a formally defined process in the modeling phase. Consequently, any {DEVS} simulation engine can be used. This paper advances the state-of-the-art in hardware-software co-design methodologies.

    @inproceedings{MMF+16,
    abstract = {Design and development of hard {Real-Time} ({RT}) embedded systems present several crucial requirements regarding criticality and timeliness of these systems. Formal methods have been presented as a promising alternative to deal with the design issues of these applications. However, these formal method do not scale well in complex systems. Modeling and Simulation ({M\&S}) provides cost-effective approaches to verify and validate the design and implementation details of complex {RT} applications. Nevertheless, {M\&S} approaches and artifacts are usually discarded in the later phases of the development. Discrete Event Systems Specification ({DEVS}) provides an appropriate {M\&S} framework to provide formal specifications to the actual {RT} system, incrementally moving from software specifications to a full hardware embedded system. In this work, we propose a hardware-in-the-loop model-driven method, based on {DEVS} for {RT}/embedded application/systems engineering. Our approach is based on an incremental substitution of {DEVS} virtual software models with Unix-compliant device files through a formally defined process in the modeling phase. Consequently, any {DEVS} simulation engine can be used. This paper advances the state-of-the-art in hardware-software co-design methodologies.},
    address = {San Diego, CA, USA},
    author = {Risco Mart\'{\i}n, Jos\'{e} L. and Mittal, Saurabh and Fabero, Juan C. and Malag\'{o}n, Pedro and Ayala, Jos\'{e} L.},
    booktitle = {Proceedings of the Summer Computer Simulation Conference},
    citeulike-article-id = {14435517},
    citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=3015619},
    isbn = {978-1-5108-2424-9},
    keywords = {greenlsi},
    location = {Montreal, Quebec, Canada},
    posted-at = {2017-09-21 15:12:45},
    priority = {2},
    publisher = {Society for Computer Simulation International},
    series = {SCSC '16},
    title = {Real-time {Hardware/Software} Co-design Using Devs-based Transparent {M\&\#38;S} Framework},
    url = {http://portal.acm.org/citation.cfm?id=3015619},
    year = {2016}
    }

  • M. Zapater, J. L. Risco-Martín, P. Arroba, J. L. Ayala, J. M. Moya, and R. Hermida, “Runtime data center temperature prediction using grammatical evolution techniques,” Applied soft computing, 2016. doi:10.1016/j.asoc.2016.07.042
    [BibTeX] [Abstract] [Download PDF]

    Modeling methodology for temperature prediction in data centers Prediction of server {CPU} and inlet temperature under variable cooling setups Development of time-dependent multi-variable models based on Grammatical Evolution. Premature convergence techniques using Social Disaster Techniques and Random {Off-Spring} Generation. Comparison to other techniques such as {ARMA}, {N4SID} and {NARX}. Models tuned, trained and tested using measurements from real server and data center traces. Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimize cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the temperature attained by servers under variable cooling setups. Due to the complex thermal dynamics of data rooms, accurate runtime data center temperature prediction has remained as an important challenge. By using Gramatical Evolution techniques, this paper presents a methodology for the generation of temperature models for data centers and the runtime prediction of {CPU} and inlet temperature under variable cooling setups. As opposed to time costly Computational Fluid Dynamics techniques, our models do not need specific knowledge about the problem, can be used in arbitrary data centers, re-trained if conditions change and have negligible overhead during runtime prediction. Our models have been trained and tested by using traces from real Data Center scenarios. Our results show how we can fully predict the temperature of the servers in a data rooms, with prediction errors below {2°C} and {0.5°C} in {CPU} and server inlet temperature respectively.

    @article{ZRA+16,
    abstract = { Modeling methodology for temperature prediction in data centers Prediction of server {CPU} and inlet temperature under variable cooling setups Development of time-dependent multi-variable models based on Grammatical Evolution. Premature convergence techniques using Social Disaster Techniques and Random {Off-Spring} Generation. Comparison to other techniques such as {ARMA}, {N4SID} and {NARX}. Models tuned, trained and tested using measurements from real server and data center traces. Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimize cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the temperature attained by servers under variable cooling setups. Due to the complex thermal dynamics of data rooms, accurate runtime data center temperature prediction has remained as an important challenge. By using Gramatical Evolution techniques, this paper presents a methodology for the generation of temperature models for data centers and the runtime prediction of {CPU} and inlet temperature under variable cooling setups. As opposed to time costly Computational Fluid Dynamics techniques, our models do not need specific knowledge about the problem, can be used in arbitrary data centers, re-trained if conditions change and have negligible overhead during runtime prediction. Our models have been trained and tested by using traces from real Data Center scenarios. Our results show how we can fully predict the temperature of the servers in a data rooms, with prediction errors below {2°C} and {0.5°C} in {CPU} and server inlet temperature respectively. },
    author = {Zapater, Marina and Risco-Mart\'{\i}n, Jos\'{e} L. and Arroba, Patricia and Ayala, Jos\'{e} L. and Moya, Jos\'{e} M. and Hermida, Rom\'{a}n},
    citeulike-article-id = {14115570},
    citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.asoc.2016.07.042},
    doi = {10.1016/j.asoc.2016.07.042},
    issn = {15684946},
    journal = {Applied Soft Computing},
    keywords = {ee, greendisc, greenlsi, josem},
    month = aug,
    posted-at = {2016-09-15 09:41:42},
    priority = {2},
    title = {Runtime Data Center Temperature Prediction using Grammatical Evolution Techniques},
    url = {http://dx.doi.org/10.1016/j.asoc.2016.07.042},
    year = {2016}
    }

  • P. Arroba, J. M. Moya, J. L. Ayala, and R. Buyya, “Proactive power and thermal aware optimizations for Energy-Efficient cloud computing,” in Design automation and test in europe. date, 2016.
    [BibTeX] [Abstract] [Download PDF]

    This work focuses on addressing the energy challenge in Cloud data centers from a thermal and power-aware perspective using proactive strategies. Our work proposes the design and implementation of models and global optimizations that jointly consider energy consumption of both computing and cooling resources while maintaining {QoS}.

    @inproceedings{AMAB16,
    abstract = {This work focuses on addressing the energy challenge in Cloud data centers from a thermal and power-aware perspective using proactive strategies. Our work proposes the design and implementation of models and global optimizations that jointly consider energy consumption of both computing and cooling resources while maintaining {QoS}.},
    author = {Arroba, Patricia and Moya, Jos\'{e} M. and Ayala, Jos\'{e} L. and Buyya, Rajkumar},
    booktitle = {Design Automation and Test in Europe. DATE},
    citeulike-article-id = {13989456},
    citeulike-linkout-0 = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7459270},
    keywords = {cloud, greendisc, greenlsi},
    posted-at = {2016-03-28 14:26:15},
    priority = {2},
    title = {Proactive Power and Thermal Aware Optimizations for {Energy-Efficient} Cloud Computing},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7459270},
    year = {2016}
    }

  • J. C. Salinas-Hilburg, M. Zapater, J. L. Risco-Martín, J. M. Moya, and J. L. Ayala, “Unsupervised power modeling of Co-Allocated workloads for energy efficiency in data centers,” in Design, automation and test in europe (date), 2016.
    [BibTeX] [Abstract] [Download PDF]

    Data centers are huge power consumers and their energy consumption keeps on rising despite the efforts to increase energy efficiency. A great body of research is devoted to the reduction of the computational power of these facilities, applying techniques such as power budgeting and power capping in servers. Such techniques rely on models to predict the power consumption of servers. However, estimating overall server power for arbitrary applications when running co-allocated in multi-threaded servers is not a trivial task. In this paper, we use Grammatical Evolution techniques to predict the dynamic power of the {CPU} and memory subsystems of an enterprise server using the hardware counters of each application. On top of our dynamic power models, we use fan and temperature-dependent leakage power models to obtain the overall server power. To train and test our models we use real traces from a presently shipping enterprise server under a wide set of sequential and parallel workloads running at various frequencies We prove that our model is able to predict the power consumption of two different tasks co-allocated in the same server, keeping error below {8W}. For the first time in literature, we develop a methodology able to combine the hardware counters of two individual applications, and estimate overall server power consumption without running the co-allocated application. Our results show a prediction error below {12W}, which represents a 7.3\% of the overall server power, outperforming previous approaches in the state of the art.

    @inproceedings{SZR+16,
    abstract = {Data centers are huge power consumers and their energy consumption keeps on rising despite the efforts to increase energy efficiency. A great body of research is devoted to the reduction of the computational power of these facilities, applying techniques such as power budgeting and power capping in servers. Such techniques rely on models to predict the power consumption of servers. However, estimating overall server power for arbitrary applications when running co-allocated in multi-threaded servers is not a trivial task. In this paper, we use Grammatical Evolution techniques to predict the dynamic power of the {CPU} and memory subsystems of an enterprise server using the hardware counters of each application. On top of our dynamic power models, we use fan and temperature-dependent leakage power models to obtain the overall server power. To train and test our models we use real traces from a presently shipping enterprise server under a wide set of sequential and parallel workloads running at various frequencies We prove that our model is able to predict the power consumption of two different tasks co-allocated in the same server, keeping error below {8W}. For the first time in literature, we develop a methodology able to combine the hardware counters of two individual applications, and estimate overall server power consumption without running the co-allocated application. Our results show a prediction error below {12W}, which represents a 7.3\% of the overall server power, outperforming previous approaches in the state of the art.},
    author = {Salinas-Hilburg, J. C. and Zapater, M. and Risco-Mart\'{\i}n, J. L. and Moya, J. M. and Ayala, J. L.},
    booktitle = {Design, Automation and Test in Europe (DATE)},
    citeulike-article-id = {13911603},
    citeulike-linkout-0 = {http://dl.acm.org/citation.cfm?id=2972121},
    citeulike-linkout-1 = {http://ieeexplore.ieee.org/document/7459518/?reload=true\&\#38;arnumber=7459518},
    isbn = {978-3-9815-3707-9},
    issn = {1558-1101},
    keywords = {ee, greenlsi, josem},
    posted-at = {2016-03-28 10:51:23},
    priority = {2},
    publisher = {EDAA},
    title = {Unsupervised Power Modeling of {Co-Allocated} Workloads for Energy Efficiency in Data Centers},
    url = {http://dl.acm.org/citation.cfm?id=2972121},
    year = {2016}
    }

  • A. Pahlevan, J. Picorel, A. P. Zarandi, D. Rossi, M. Zapater, A. Bartolini, P. G. del Valle, D. Atienza, L. Benini, and B. Falsafi, “Towards Near-Threshold server processors,” in Design automation and test in europe (date), 2016.
    [BibTeX] [Abstract] [Download PDF]

    The popularity of cloud computing has led to a dramatic increase in the number of data centers in the world. The ever-increasing computational demands along with the slowdown in technology scaling has ushered an era of power-limited servers. Techniques such as near-threshold computing ({NTC}) can be used to improve energy efficiency in the {post-Dennard} scaling era. This paper describes an architecture based on the {FD}-{SOI} process technology for near-threshold operation in servers. Our work explores the trade-offs in energy and performance when running a wide range of applications found in private and public clouds, ranging from traditional scale-out applications, such as web search or media streaming, to virtualized banking applications. Our study demonstrates the benefits of near-threshold operation and proposes several directions to synergistically increase the energy proportionality of a near-threshold server.

    @inproceedings{PPZ+16,
    abstract = {The popularity of cloud computing has led to a dramatic increase in the number of data centers in the world. The ever-increasing computational demands along with the slowdown in technology scaling has ushered an era of power-limited servers. Techniques such as near-threshold computing ({NTC}) can be used to improve energy efficiency in the {post-Dennard} scaling era. This paper describes an architecture based on the {FD}-{SOI} process technology for near-threshold operation in servers. Our work explores the trade-offs in energy and performance when running a wide range of applications found in private and public clouds, ranging from traditional scale-out applications, such as web search or media streaming, to virtualized banking applications. Our study demonstrates the benefits of near-threshold operation and proposes several directions to synergistically increase the energy proportionality of a near-threshold server.},
    author = {Pahlevan, Ali and Picorel, Javier and Zarandi, Arash P. and Rossi, Davide and Zapater, Marina and Bartolini, Andrea and del Valle, Pablo G. and Atienza, David and Benini, Luca and Falsafi, Babak},
    booktitle = {Design Automation and Test in Europe (DATE)},
    citeulike-article-id = {13938246},
    citeulike-linkout-0 = {http://infoscience.epfl.ch/record/215306},
    keywords = {ee, epfl, greenlsi},
    month = mar,
    posted-at = {2016-03-28 10:50:43},
    priority = {2},
    title = {Towards {Near-Threshold} Server Processors},
    url = {http://infoscience.epfl.ch/record/215306},
    year = {2016}
    }

2015

  • M. Zapater, “Proactive and reactive thermal aware optimization techniques to minimize the environmental impact of data centers,” PhD Thesis, 2015.
    [BibTeX] [Abstract] [Download PDF]

    Los Centros de Datos se encuentran actualmente en cualquier sector de la economía mundial. Están compuestos por miles de servidores, dando servicio a los usuarios de forma global, las 24 horas del día y los 365 días del año. Durante los últimos años, las aplicaciones del ámbito de la {e-Ciencia}, como la {e-Salud} o las Ciudades Inteligentes han experimentado un desarrollo muy significativo. La necesidad de manejar de forma eficiente las necesidades de cómputo de aplicaciones de nueva generación, junto con la creciente demanda de recursos en aplicaciones tradicionales, han facilitado el rápido crecimiento y la proliferación de los Centros de Datos. El principal inconveniente de este aumento de capacidad ha sido el rápido y dramático incremento del consumo energético de estas infraestructuras. En 2010, la factura eléctrica de los Centros de Datos representaba el 1.3\% del consumo eléctrico mundial. Sólo en el año 2012, el consumo de potencia de los Centros de Datos creció un 63\%, alcanzando los {38GW}. En 2013 se estimó un crecimiento de otro 17\%, hasta llegar a los {43GW}. Además, los Centros de Datos son responsables de más del 2\% del total de emisiones de dióxido de carbono a la atmósfera. Esta tesis doctoral se enfrenta al problema energético proponiendo técnicas proactivas y reactivas conscientes de la temperatura y de la energía, que contribuyen a tener Centros de Datos más eficientes. Este trabajo desarrolla modelos de energía y utiliza el conocimiento sobre la demanda energética de la carga de trabajo a ejecutar y de los recursos de computación y refrigeración del Centro de Datos para optimizar el consumo. Además, los Centros de Datos son considerados como un elemento crucial dentro del marco de la aplicación ejecutada, optimizando no sólo el consumo del Centro de Datos sino el consumo energético global de la aplicación. Los principales componentes del consumo en los Centros de Datos son la potencia de computación utilizada por los equipos de {IT}, y la refrigeración necesaria para mantener los servidores dentro de un rango de temperatura de trabajo que asegure su correcto funcionamiento. Debido a la relación cúbica entre la velocidad de los ventiladores y el consumo de los mismos, las soluciones basadas en el sobre-aprovisionamiento de aire frío al servidor generalmente tienen como resultado ineficiencias energéticas. Por otro lado, temperaturas más elevadas en el procesador llevan a un consumo de fugas mayor, debido a la relación exponencial del consumo de fugas con la temperatura. Además, las características de la carga de trabajo y las políticas de asignación de recursos tienen un impacto importante en los balances entre corriente de fugas y consumo de refrigeración. La primera gran contribución de este trabajo es el desarrollo de modelos de potencia y temperatura que permiten describes estos balances entre corriente de fugas y refrigeración; así como la propuesta de estrategias para minimizar el consumo del servidor por medio de la asignación conjunta de refrigeración y carga desde una perspectiva multivariable. Cuando escalamos a nivel del Centro de Datos, observamos un comportamiento similar en términos del balance entre corrientes de fugas y refrigeración. Conforme aumenta la temperatura de la sala, mejora la eficiencia de la refrigeración. Sin embargo, este incremente de la temperatura de sala provoca un aumento en la temperatura de la {CPU} y, por tanto, también del consumo de fugas. Además, la dinámica de la sala tiene un comportamiento muy desigual, no equilibrado, debido a la asignación de carga y a la heterogeneidad en el equipamiento de {IT}. La segunda contribución de esta tesis es la propuesta de técnicas de asigación conscientes de la temperatura y heterogeneidad que permiten optimizar conjuntamente la asignación de tareas y refrigeración a los servidores. Estas estrategias necesitan estar respaldadas por modelos flexibles, que puedan trabajar en tiempo real, para describir el sistema desde un nivel de abstracción alto. Dentro del ámbito de las aplicaciones de nueva generación, las decisiones tomadas en el nivel de aplicación pueden tener un impacto dramático en el consumo energético de niveles de abstracción menores, como por ejemplo, en el Centro de Datos. Es importante considerar las relaciones entre todos los agentes computacionales implicados en el problema, de forma que puedan cooperar para conseguir el objetivo común de reducir el coste energético global del sistema. La tercera contribución de esta tesis es el desarrollo de optimizaciones energéticas para la aplicación global por medio de la evaluación de los costes de ejecutar parte del procesado necesario en otros niveles de abstracción, que van desde los nodos hasta el Centro de Datos, por medio de técnicas de balanceo de carga. Como resumen, el trabajo presentado en esta tesis lleva a cabo contribuciones en el modelado y optimización consciente del consumo por fugas y la refrigeración de servidores; el modelado de los Centros de Datos y el desarrollo de políticas de asignación conscientes de la heterogeneidad; y desarrolla mecanismos para la optimización energética de aplicaciones de nueva generación desde varios niveles de abstracción. {ABSTRACT} Data centers are easily found in every sector of the worldwide economy. They consist of tens of thousands of servers, serving millions of users globally and 24-7. In the last years, {e-Science} applications such {e-Health} or Smart Cities have experienced a significant development. The need to deal efficiently with the computational needs of next-generation applications together with the increasing demand for higher resources in traditional applications has facilitated the rapid proliferation and growing of data centers. A drawback to this capacity growth has been the rapid increase of the energy consumption of these facilities. In 2010, data center electricity represented 1.3\% of all the electricity use in the world. In year 2012 alone, global data center power demand grew 63\% to {38GW}. A further rise of 17\% to {43GW} was estimated in 2013. Moreover, data centers are responsible for more than 2\% of total carbon dioxide emissions. This {PhD} Thesis addresses the energy challenge by proposing proactive and reactive thermal and energy-aware optimization techniques that contribute to place data centers on a more scalable curve. This work develops energy models and uses the knowledge about the energy demand of the workload to be executed and the computational and cooling resources available at data center to optimize energy consumption. Moreover, data centers are considered as a crucial element within their application framework, optimizing not only the energy consumption of the facility, but the global energy consumption of the application. The main contributors to the energy consumption in a data center are the computing power drawn by {IT} equipment and the cooling power needed to keep the servers within a certain temperature range that ensures safe operation. Because of the cubic relation of fan power with fan speed, solutions based on over-provisioning cold air into the server usually lead to inefficiencies. On the other hand, higher chip temperatures lead to higher leakage power because of the exponential dependence of leakage on temperature. Moreover, workload characteristics as well as allocation policies also have an important impact on the leakage-cooling tradeoffs. The first key contribution of this work is the development of power and temperature models that accurately describe the leakage-cooling tradeoffs at the server level, and the proposal of strategies to minimize server energy via joint cooling and workload management from a multivariate perspective. When scaling to the data center level, a similar behavior in terms of leakage-temperature tradeoffs can be observed. As room temperature raises, the efficiency of data room cooling units improves. However, as we increase room temperature, {CPU} temperature raises and so does leakage power. Moreover, the thermal dynamics of a data room exhibit unbalanced patterns due to both the workload allocation and the heterogeneity of computing equipment. The second main contribution is the proposal of thermal- and heterogeneity-aware workload management techniques that jointly optimize the allocation of computation and cooling to servers. These strategies need to be backed up by flexible room level models, able to work on runtime, that describe the system from a high level perspective. Within the framework of next-generation applications, decisions taken at this scope can have a dramatical impact on the energy consumption of lower abstraction levels, i.e. the data center facility. It is important to consider the relationships between all the computational agents involved in the problem, so that they can cooperate to achieve the common goal of reducing energy in the overall system. The third main contribution is the energy optimization of the overall application by evaluating the energy costs of performing part of the processing in any of the different abstraction layers, from the node to the data center, via workload management and off-loading techniques. In summary, the work presented in this {PhD} Thesis, makes contributions on leakage and cooling aware server modeling and optimization, data center thermal modeling and heterogeneityaware data center resource allocation, and develops mechanisms for the energy optimization for next-generation applications from a multi-layer perspective.

    @phdthesis{Z16,
    abstract = {Los Centros de Datos se encuentran actualmente en cualquier sector de la econom\'{\i}a mundial. Est\'{a}n compuestos por miles de servidores, dando servicio a los usuarios de forma global, las 24 horas del d\'{\i}a y los 365 d\'{\i}as del a\~{n}o. Durante los \'{u}ltimos a\~{n}os, las aplicaciones del \'{a}mbito de la {e-Ciencia}, como la {e-Salud} o las Ciudades Inteligentes han experimentado un desarrollo muy significativo. La necesidad de manejar de forma eficiente las necesidades de c\'{o}mputo de aplicaciones de nueva generaci\'{o}n, junto con la creciente demanda de recursos en aplicaciones tradicionales, han facilitado el r\'{a}pido crecimiento y la proliferaci\'{o}n de los Centros de Datos. El principal inconveniente de este aumento de capacidad ha sido el r\'{a}pido y dram\'{a}tico incremento del consumo energ\'{e}tico de estas infraestructuras. En 2010, la factura el\'{e}ctrica de los Centros de Datos representaba el 1.3\% del consumo el\'{e}ctrico mundial. S\'{o}lo en el a\~{n}o 2012, el consumo de potencia de los Centros de Datos creci\'{o} un 63\%, alcanzando los {38GW}. En 2013 se estim\'{o} un crecimiento de otro 17\%, hasta llegar a los {43GW}. Adem\'{a}s, los Centros de Datos son responsables de m\'{a}s del 2\% del total de emisiones de di\'{o}xido de carbono a la atm\'{o}sfera. Esta tesis doctoral se enfrenta al problema energ\'{e}tico proponiendo t\'{e}cnicas proactivas y reactivas conscientes de la temperatura y de la energ\'{\i}a, que contribuyen a tener Centros de Datos m\'{a}s eficientes. Este trabajo desarrolla modelos de energ\'{\i}a y utiliza el conocimiento sobre la demanda energ\'{e}tica de la carga de trabajo a ejecutar y de los recursos de computaci\'{o}n y refrigeraci\'{o}n del Centro de Datos para optimizar el consumo. Adem\'{a}s, los Centros de Datos son considerados como un elemento crucial dentro del marco de la aplicaci\'{o}n ejecutada, optimizando no s\'{o}lo el consumo del Centro de Datos sino el consumo energ\'{e}tico global de la aplicaci\'{o}n. Los principales componentes del consumo en los Centros de Datos son la potencia de computaci\'{o}n utilizada por los equipos de {IT}, y la refrigeraci\'{o}n necesaria para mantener los servidores dentro de un rango de temperatura de trabajo que asegure su correcto funcionamiento. Debido a la relaci\'{o}n c\'{u}bica entre la velocidad de los ventiladores y el consumo de los mismos, las soluciones basadas en el sobre-aprovisionamiento de aire fr\'{\i}o al servidor generalmente tienen como resultado ineficiencias energ\'{e}ticas. Por otro lado, temperaturas m\'{a}s elevadas en el procesador llevan a un consumo de fugas mayor, debido a la relaci\'{o}n exponencial del consumo de fugas con la temperatura. Adem\'{a}s, las caracter\'{\i}sticas de la carga de trabajo y las pol\'{\i}ticas de asignaci\'{o}n de recursos tienen un impacto importante en los balances entre corriente de fugas y consumo de refrigeraci\'{o}n. La primera gran contribuci\'{o}n de este trabajo es el desarrollo de modelos de potencia y temperatura que permiten describes estos balances entre corriente de fugas y refrigeraci\'{o}n; as\'{\i} como la propuesta de estrategias para minimizar el consumo del servidor por medio de la asignaci\'{o}n conjunta de refrigeraci\'{o}n y carga desde una perspectiva multivariable. Cuando escalamos a nivel del Centro de Datos, observamos un comportamiento similar en t\'{e}rminos del balance entre corrientes de fugas y refrigeraci\'{o}n. Conforme aumenta la temperatura de la sala, mejora la eficiencia de la refrigeraci\'{o}n. Sin embargo, este incremente de la temperatura de sala provoca un aumento en la temperatura de la {CPU} y, por tanto, tambi\'{e}n del consumo de fugas. Adem\'{a}s, la din\'{a}mica de la sala tiene un comportamiento muy desigual, no equilibrado, debido a la asignaci\'{o}n de carga y a la heterogeneidad en el equipamiento de {IT}. La segunda contribuci\'{o}n de esta tesis es la propuesta de t\'{e}cnicas de asigaci\'{o}n conscientes de la temperatura y heterogeneidad que permiten optimizar conjuntamente la asignaci\'{o}n de tareas y refrigeraci\'{o}n a los servidores. Estas estrategias necesitan estar respaldadas por modelos flexibles, que puedan trabajar en tiempo real, para describir el sistema desde un nivel de abstracci\'{o}n alto. Dentro del \'{a}mbito de las aplicaciones de nueva generaci\'{o}n, las decisiones tomadas en el nivel de aplicaci\'{o}n pueden tener un impacto dram\'{a}tico en el consumo energ\'{e}tico de niveles de abstracci\'{o}n menores, como por ejemplo, en el Centro de Datos. Es importante considerar las relaciones entre todos los agentes computacionales implicados en el problema, de forma que puedan cooperar para conseguir el objetivo com\'{u}n de reducir el coste energ\'{e}tico global del sistema. La tercera contribuci\'{o}n de esta tesis es el desarrollo de optimizaciones energ\'{e}ticas para la aplicaci\'{o}n global por medio de la evaluaci\'{o}n de los costes de ejecutar parte del procesado necesario en otros niveles de abstracci\'{o}n, que van desde los nodos hasta el Centro de Datos, por medio de t\'{e}cnicas de balanceo de carga. Como resumen, el trabajo presentado en esta tesis lleva a cabo contribuciones en el modelado y optimizaci\'{o}n consciente del consumo por fugas y la refrigeraci\'{o}n de servidores; el modelado de los Centros de Datos y el desarrollo de pol\'{\i}ticas de asignaci\'{o}n conscientes de la heterogeneidad; y desarrolla mecanismos para la optimizaci\'{o}n energ\'{e}tica de aplicaciones de nueva generaci\'{o}n desde varios niveles de abstracci\'{o}n. {ABSTRACT} Data centers are easily found in every sector of the worldwide economy. They consist of tens of thousands of servers, serving millions of users globally and 24-7. In the last years, {e-Science} applications such {e-Health} or Smart Cities have experienced a significant development. The need to deal efficiently with the computational needs of next-generation applications together with the increasing demand for higher resources in traditional applications has facilitated the rapid proliferation and growing of data centers. A drawback to this capacity growth has been the rapid increase of the energy consumption of these facilities. In 2010, data center electricity represented 1.3\% of all the electricity use in the world. In year 2012 alone, global data center power demand grew 63\% to {38GW}. A further rise of 17\% to {43GW} was estimated in 2013. Moreover, data centers are responsible for more than 2\% of total carbon dioxide emissions. This {PhD} Thesis addresses the energy challenge by proposing proactive and reactive thermal and energy-aware optimization techniques that contribute to place data centers on a more scalable curve. This work develops energy models and uses the knowledge about the energy demand of the workload to be executed and the computational and cooling resources available at data center to optimize energy consumption. Moreover, data centers are considered as a crucial element within their application framework, optimizing not only the energy consumption of the facility, but the global energy consumption of the application. The main contributors to the energy consumption in a data center are the computing power drawn by {IT} equipment and the cooling power needed to keep the servers within a certain temperature range that ensures safe operation. Because of the cubic relation of fan power with fan speed, solutions based on over-provisioning cold air into the server usually lead to inefficiencies. On the other hand, higher chip temperatures lead to higher leakage power because of the exponential dependence of leakage on temperature. Moreover, workload characteristics as well as allocation policies also have an important impact on the leakage-cooling tradeoffs. The first key contribution of this work is the development of power and temperature models that accurately describe the leakage-cooling tradeoffs at the server level, and the proposal of strategies to minimize server energy via joint cooling and workload management from a multivariate perspective. When scaling to the data center level, a similar behavior in terms of leakage-temperature tradeoffs can be observed. As room temperature raises, the efficiency of data room cooling units improves. However, as we increase room temperature, {CPU} temperature raises and so does leakage power. Moreover, the thermal dynamics of a data room exhibit unbalanced patterns due to both the workload allocation and the heterogeneity of computing equipment. The second main contribution is the proposal of thermal- and heterogeneity-aware workload management techniques that jointly optimize the allocation of computation and cooling to servers. These strategies need to be backed up by flexible room level models, able to work on runtime, that describe the system from a high level perspective. Within the framework of next-generation applications, decisions taken at this scope can have a dramatical impact on the energy consumption of lower abstraction levels, i.e. the data center facility. It is important to consider the relationships between all the computational agents involved in the problem, so that they can cooperate to achieve the common goal of reducing energy in the overall system. The third main contribution is the energy optimization of the overall application by evaluating the energy costs of performing part of the processing in any of the different abstraction layers, from the node to the data center, via workload management and off-loading techniques. In summary, the work presented in this {PhD} Thesis, makes contributions on leakage and cooling aware server modeling and optimization, data center thermal modeling and heterogeneityaware data center resource allocation, and develops mechanisms for the energy optimization for next-generation applications from a multi-layer perspective.},
    author = {Zapater, Marina},
    citeulike-article-id = {14137438},
    citeulike-linkout-0 = {http://oa.upm.es/38700/},
    keywords = {greenlsi},
    posted-at = {2016-09-15 10:27:17},
    priority = {2},
    school = {Universidad Polit\'{e}cnica de Madrid},
    title = {Proactive and reactive thermal aware optimization techniques to minimize the environmental impact of data centers},
    url = {http://oa.upm.es/38700/},
    year = {2015}
    }

  • P. Arroba, J. M. Moya, J. L. Ayala, and R. Buyya, “DVFS-­aware consolidation for Energy-Efficient clouds,” in 2015 international conference on parallel architecture and compilation pact 2015, 2015. doi:10.1109/PACT.2015.59
    [BibTeX] [Abstract] [Download PDF]

    Nowadays, data centers consume about 2\% of the worldwide energy production, originating more than 43 million tons of {CO2} per year. Cloud providers need to implement an energy-efficient management of physical resources in order to meet the growing demand for their services and ensure minimal costs. From the application-framework viewpoint, Cloud workloads present additional restrictions as 24/7 availability, and {SLA} constraints among others. Also, workload variation impacts on the performance of two of the main strategies for energy-efficiency in Cloud data centers: Dynamic Voltage and Frequency Scaling ({DVFS}) and Consolidation. Our work proposes two contributions: 1) a {DVFS} policy that takes into account the trade-offs between energy consumption and performance degradation; 2) a novel consolidation algorithm that is aware of the frequency that would be necessary when allocating a Cloud workload in order to maintain {QoS}. Our results demonstrate that including {DVFS} awareness in workload management provides substantial energy savings of up to 39.14\% for scenarios under dynamic workload conditions.

    @inproceedings{AMAB15,
    abstract = {Nowadays, data centers consume about 2\% of the worldwide energy production, originating more than 43 million tons of {CO2} per year. Cloud providers need to implement an energy-efficient management of physical resources in order to meet the growing demand for their services and ensure minimal costs. From the application-framework viewpoint, Cloud workloads present additional restrictions as 24/7 availability, and {SLA} constraints among others. Also, workload variation impacts on the performance of two of the main strategies for energy-efficiency in Cloud data centers:
    Dynamic Voltage and Frequency Scaling ({DVFS}) and Consolidation. Our work proposes two contributions: 1) a {DVFS} policy that takes into account the trade-offs between energy consumption and performance degradation; 2) a novel
    consolidation algorithm that is aware of the frequency that would be necessary when allocating a Cloud workload in order to maintain {QoS}. Our results demonstrate that including {DVFS} awareness in workload management provides substantial energy savings of up to 39.14\% for scenarios under dynamic workload conditions.},
    author = {Arroba, Patricia and Moya, Jos\'{e} M. and Ayala, Jos\'{e} L. and Buyya, Rajkumar},
    booktitle = {2015 International Conference on Parallel Architecture and Compilation PACT 2015},
    citeulike-article-id = {13989454},
    citeulike-linkout-0 = {http://dx.doi.org/10.1109/PACT.2015.59},
    doi = {10.1109/PACT.2015.59},
    keywords = {cloud, greendisc, greenlsi},
    posted-at = {2016-03-28 14:26:43},
    priority = {2},
    title = {{DVFS}-­Aware Consolidation for {Energy-Efficient} Clouds},
    url = {http://dx.doi.org/10.1109/PACT.2015.59},
    year = {2015}
    }

  • J. Pagán, M. De Orbe, A. Gago, M. Sobrado, J. Risco-Martín, J. Mora, J. Moya, and J. Ayala, “Robust and accurate modeling approaches for migraine Per-Patient prediction from ambulatory data,” Sensors, vol. 15, iss. 7, pp. 15419-15442, 2015. doi:10.3390/s150715419
    [BibTeX] [Abstract] [Download PDF]

    Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network ({WBSN}). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models ({N4SID}) that are capable of providing average forecast windows of 47 min and a low rate of false positives.

    @article{POG+15,
    abstract = {Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network ({WBSN}). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models ({N4SID}) that are capable of providing average forecast windows of 47 min and a low rate of false positives.},
    author = {Pag\'{a}n, Josu\'{e} and De Orbe, M. and Gago, Ana and Sobrado, M\'{o}nica and Risco-Mart\'{\i}n, Jos\'{e} and Mora, J. and Moya, Jos\'{e} and Ayala, Jos\'{e}},
    citeulike-article-id = {13661876},
    citeulike-linkout-0 = {http://dx.doi.org/10.3390/s150715419},
    citeulike-linkout-1 = {http://www.mdpi.com/1424-8220/15/7/15419},
    citeulike-linkout-2 = {http://www.mdpi.com/1424-8220/15/7/15419/pdf},
    day = {30},
    doi = {10.3390/s150715419},
    journal = {Sensors},
    keywords = {greenlsi, migraine, modeling, n4sid, prediction, robustness, webs},
    month = jun,
    number = {7},
    pages = {15419--15442},
    posted-at = {2016-03-28 11:04:02},
    priority = {2},
    title = {Robust and Accurate Modeling Approaches for Migraine {Per-Patient} Prediction from Ambulatory Data},
    url = {http://dx.doi.org/10.3390/s150715419},
    volume = {15},
    year = {2015}
    }

  • P. Malagón, J. de Goyeneche, D. Fraga, and J. M. Moya, “Bitslice software implementation of KeeLoq as a side-channel countermeasure,” in Proceedings of the wess’15: workshop on embedded systems security, New York, NY, USA, 2015. doi:10.1145/2818362.2818366
    [BibTeX] [Abstract] [Download PDF]

    Bitslice is a non-conventional way to implement algorithms using a scalar processor as a {SIMD}. It involves breaking down the algorithm into logical bit operations so that N parallel <operations are possible on a single N-bit microprocessor. It is applied to encryption algorithms, processing N consecutive blocks simultaneously, to achieve high throughput. Security applications using the {KeeLoq} algorithm are not suitable to traditional bitslice implementations because usually there are no N blocks to be processed. We propose a {KeeLoq} bitslice implementation, derived from its Algebraic Normal Form, for a single input block as a countermeasure against side-channel attacks. Our experimental results show there is no timing information leaked with an improvement factor of 3.01 in executed cycles. However, the implementation is still vulnerable to differential side-channel analysis, so we propose a secured variation that increases the resistance against differential power analysis without timing leakage, with a lower improvement factor of 1.21 in executed cycles.

    @inproceedings{MGFM15,
    abstract = {Bitslice is a non-conventional way to implement algorithms using a scalar processor as a {SIMD}. It involves breaking down the algorithm into logical bit operations so that N parallel <operations are possible on a single N-bit microprocessor. It is applied to encryption algorithms, processing N consecutive blocks simultaneously, to achieve high throughput. Security applications using the {KeeLoq} algorithm are not suitable to traditional bitslice implementations because usually there are no N blocks to be processed. We propose a {KeeLoq} bitslice implementation, derived from its Algebraic Normal Form, for a single input block as a countermeasure against side-channel attacks. Our experimental results show there is no timing information leaked with an improvement factor of 3.01 in executed cycles. However, the implementation is still vulnerable to differential side-channel analysis, so we propose a secured variation that increases the resistance against differential power analysis without timing leakage, with a lower improvement factor of 1.21 in executed cycles.},
    address = {New York, NY, USA},
    author = {Malag\'{o}n, Pedro and de Goyeneche, Juan-Mariano and Fraga, David and Moya, Jos\'{e} M.},
    booktitle = {Proceedings of the WESS'15: Workshop on Embedded Systems Security},
    citeulike-article-id = {13815310},
    citeulike-linkout-0 = {http://dx.doi.org/10.1145/2818362.2818366},
    citeulike-linkout-1 = {http://doi.acm.org/10.1145/2818362.2818366},
    doi = {10.1145/2818362.2818366},
    keywords = {anf, bitslice, cpa, greenlsi, josem, keeloq, malagon, nlfsr, sca},
    location = {Amsterdam, Netherlands},
    posted-at = {2015-10-28 06:12:12},
    priority = {0},
    publisher = {ACM},
    series = {WESS'15},
    title = {Bitslice Software Implementation of {KeeLoq} As a Side-channel Countermeasure},
    url = {http://doi.acm.org/10.1145/2818362.2818366},
    year = {2015}
    }

  • R. Cattaneo, G. C. Durelli, J. Pagán, M. Zapater, M. Ferroni, A. Nacci, M. Vallejo, M. D. Santambrogio, J. L. Ayala, and S. Campanoni, “Power-awareness and smart-resource management in embedded computing systems,” in International conference on hardware/software codesign and system synthesis, 2015.
    [BibTeX] [Abstract] [Download PDF]

    Resources such as quantities of transistors and memory, the level of integration and the speed of components have increased dramatically over the years. Even though the technologies have improved, we continue to apply outdated approaches to our use of these resources. Key computer science abstractions have not changed since the 1960’s. Therefore this is the time for a fresh approach to the way systems are designed and used.

    @inproceedings{CDP+15,
    abstract = {Resources such as quantities of transistors and memory, the level of integration and the speed of components have increased dramatically over the years. Even though the technologies have improved, we continue to apply outdated approaches to our use of these resources. Key computer science abstractions have not changed since the 1960's. Therefore this is the time for a fresh approach to the way systems are designed and used.},
    author = {Cattaneo, R. and Durelli, G. C. and Pag\'{a}n, Josu\'{e} and Zapater, Marina and Ferroni, M. and Nacci, A. and Vallejo, M. and Santambrogio, M. D. and Ayala, Jos\'{e} L. and Campanoni, Simone},
    booktitle = {International Conference on Hardware/Software Codesign and System Synthesis},
    citeulike-article-id = {13697661},
    citeulike-linkout-0 = {http://dl.acm.org/citation.cfm?id=2830851},
    howpublished = {[Accepted, to appear in 2015]},
    keywords = {artecs, ee, greenlsi},
    month = oct,
    posted-at = {2015-10-28 05:48:15},
    priority = {2},
    title = {Power-awareness and smart-resource management in embedded computing systems},
    url = {http://dl.acm.org/citation.cfm?id=2830851},
    year = {2015}
    }

  • M. Zapater, A. Turk, J. M. Moya, J. L. Ayala, and A. K. Coskun, “Dynamic workload and cooling management in High-Efficiency data centers,” in International green and sustainable computing conference (igsc), 2015. doi:10.1109/IGCC.2015.7393715
    [BibTeX] [Abstract] [Download PDF]

    Energy efficiency research in data centers has traditionally focused on raised-floor air-cooled facilities. As rack power density increases, traditional cooling is being replaced by close-coupled systems that provide enhanced airflow and cooling capacity. This work presents a model for close-coupled data centers with free cooling, and explores the power consumption trade-offs in these facilities as outdoor temperature changes throughout the year. Using this model, we propose a technique that jointly allocates workload and controls cooling in a power-efficient way. Our technique is tested with configuration parameters, power traces, and weather data collected from real-life data centers, and application profiles obtained from enterprise servers. Results show that our joint workload allocation and cooling policy provides 5\% reduction in overall data center energy consumption, and up to 24\% peak power reduction, leading to a 6\% decrease in the electricity costs without affecting performance.

    @inproceedings{ZTM+15,
    abstract = {Energy efficiency research in data centers has traditionally focused on raised-floor air-cooled facilities. As rack power density increases, traditional cooling is being replaced by close-coupled systems that provide enhanced airflow and cooling capacity. This work presents a model for close-coupled data centers with free cooling, and explores the power consumption trade-offs in these facilities as outdoor temperature changes throughout the year. Using this model, we propose a technique that jointly allocates workload and controls cooling in a power-efficient way. Our technique is tested with configuration parameters, power traces, and weather data collected from real-life data centers, and application profiles obtained from enterprise servers. Results show that our joint workload allocation and cooling policy provides 5\% reduction in overall data center energy consumption, and up to 24\% peak power reduction, leading to a 6\% decrease in the electricity costs without affecting performance.},
    author = {Zapater, Marina and Turk, Ata and Moya, Jos\'{e} M. and Ayala, Jos\'{e} L. and Coskun, Ayse K.},
    booktitle = {International Green and Sustainable Computing Conference (IGSC)},
    citeulike-article-id = {13697663},
    citeulike-linkout-0 = {http://dx.doi.org/10.1109/IGCC.2015.7393715},
    doi = {10.1109/IGCC.2015.7393715},
    howpublished = {[Accepted, to appear in 2015]},
    keywords = {ee, greenlsi, josem},
    month = dec,
    posted-at = {2015-10-28 05:47:39},
    priority = {2},
    title = {Dynamic Workload and Cooling Management in {High-Efficiency} Data Centers},
    url = {http://dx.doi.org/10.1109/IGCC.2015.7393715},
    year = {2015}
    }

  • M. Zapater, D. Fraga, P. Malagón, Z. Banković, and J. M. Moya, “Self-organizing maps versus growing neural gas in detecting anomalies in data centres,” Logic journal of igpl, p. jzv008+, 2015. doi:10.1093/jigpal/jzv008
    [BibTeX] [Abstract] [Download PDF]

    Reliability is one of the key performance factors in data centres. The out-of-scale energy costs of these facilities lead data centre operators to increase the ambient temperature of the data room to decrease cooling costs. However, increasing ambient temperature reduces the safety margins and can result in a higher number of anomalous events. Anomalies in the data centre need to be detected as soon as possible to optimize cooling efficiency and mitigate the harmful effects over servers. This article proposes the usage of clustering-based outlier detection techniques coupled with a trust and reputation system engine to detect anomalies in data centres. We show how self-organizing maps or growing neural gas can be applied to detect cooling and workload anomalies, respectively, in a real data centre scenario with very good detection and isolation rates, in a way that is robust to the malfunction of the sensors that gather server and environmental information.

    @article{ZFM+15,
    abstract = {Reliability is one of the key performance factors in data centres. The out-of-scale energy costs of these facilities lead data centre operators to increase the ambient temperature of the data room to decrease cooling costs. However, increasing ambient temperature reduces the safety margins and can result in a higher number of anomalous events. Anomalies in the data centre need to be detected as soon as possible to optimize cooling efficiency and mitigate the harmful effects over servers. This article proposes the usage of clustering-based outlier detection techniques coupled with a trust and reputation system engine to detect anomalies in data centres. We show how self-organizing maps or growing neural gas can be applied to detect cooling and workload anomalies, respectively, in a real data centre scenario with very good detection and isolation rates, in a way that is robust to the malfunction of the sensors that gather server and environmental information.},
    author = {Zapater, M. and Fraga, D. and Malag\'{o}n, P. and Bankovi\'{c}, Z. and Moya, J. M.},
    citeulike-article-id = {13581774},
    citeulike-linkout-0 = {http://dx.doi.org/10.1093/jigpal/jzv008},
    citeulike-linkout-1 = {http://jigpal.oxfordjournals.org/content/early/2015/04/01/jigpal.jzv008.abstract},
    citeulike-linkout-2 = {http://jigpal.oxfordjournals.org/content/early/2015/04/01/jigpal.jzv008.full.pdf},
    day = {2},
    doi = {10.1093/jigpal/jzv008},
    issn = {1368-9894},
    journal = {Logic Journal of IGPL},
    keywords = {anomalies, ee, greenlsi, josem},
    month = apr,
    pages = {jzv008+},
    posted-at = {2015-08-07 08:51:39},
    priority = {2},
    publisher = {Oxford University Press},
    title = {Self-organizing Maps versus Growing Neural Gas in Detecting Anomalies in Data Centres},
    url = {http://dx.doi.org/10.1093/jigpal/jzv008},
    year = {2015}
    }

  • M. Zapater, O. Tuncer, J. Ayala, J. Moya, K. Vaidyanathan, K. Gross, and A. K. Coskun, “Leakage-Aware cooling management for improving server energy efficiency,” Ieee transactions on parallel distributed systems, vol. 26, iss. 10, pp. 2764-2777, 2015. doi:10.1109/tpds.2014.2361519
    [BibTeX] [Abstract] [Download PDF]

    The computational and cooling power demands of enterprise servers are increasing at an unsustainable rate. Understanding the relationship between computational power, temperature, leakage, and cooling power is crucial to enable energy-efficient operation at the server and data center levels. This paper develops empirical models to estimate the contributions of static and dynamic power consumption in enterprise servers for a wide range of workloads, and analyzes the interactions between temperature, leakage, and cooling power for various workload allocation policies. We propose a cooling management policy that minimizes the server energy consumption by setting the optimum fan speed during runtime. Our experimental results on a presently shipping enterprise server demonstrate that including leakage awareness in workload and cooling management provides additional energy savings without any impact on performance.

    @article{ZTA+15,
    abstract = {The computational and cooling power demands of enterprise servers are increasing at an unsustainable rate. Understanding the relationship between computational power, temperature, leakage, and cooling power is crucial to enable energy-efficient operation at the server and data center levels. This paper develops empirical models to estimate the contributions of static and dynamic power consumption in enterprise servers for a wide range of workloads, and analyzes the interactions between temperature, leakage, and cooling power for various workload allocation policies. We propose a cooling management policy that minimizes the server energy consumption by setting the optimum fan speed during runtime. Our experimental results on a presently shipping enterprise server demonstrate that including leakage awareness in workload and cooling management provides additional energy savings without any impact on performance.},
    author = {Zapater, Marina and Tuncer, Ozan and Ayala, Jose and Moya, Jose and Vaidyanathan, Karthikeyan and Gross, Kenny and Coskun, Ayse K.},
    citeulike-article-id = {13696417},
    citeulike-linkout-0 = {http://dx.doi.org/10.1109/tpds.2014.2361519},
    citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=6915867},
    doi = {10.1109/tpds.2014.2361519},
    institution = {Marina Zapater is with the (CEI Campus Moncloa UCM-UPM, Madrid 28040, Spain (e-mail: marina@die.upm.es).},
    issn = {1045-9219},
    journal = {IEEE Transactions on Parallel Distributed Systems},
    keywords = {dc, ee, greenlsi, josem},
    number = {10},
    pages = {2764--2777},
    posted-at = {2015-08-07 08:50:45},
    priority = {2},
    publisher = {IEEE},
    title = {{Leakage-Aware} Cooling Management for Improving Server Energy Efficiency},
    url = {http://dx.doi.org/10.1109/tpds.2014.2361519},
    volume = {26},
    year = {2015}
    }

  • M. Zapater, P. Arroba, J. L. Ayala, K. Olcoz, and J. M. Moya, “Energy-Aware policies in ubiquitous computing facilities,” in Cloud computing with e-science applications, CRC Press, 2015, p. 267-286+. doi:10.1201/b18021-13
    [BibTeX] [Download PDF]
    @incollection{ZAA+15,
    author = {Zapater, Marina and Arroba, Patricia and Ayala, Jos\'{e} L. and Olcoz, Katzalin and Moya, Jos\'{e} M.},
    booktitle = {Cloud Computing with e-Science Applications},
    citeulike-article-id = {13696422},
    citeulike-linkout-0 = {http://dx.doi.org/10.1201/b18021-13},
    day = {8},
    doi = {10.1201/b18021-13},
    isbn = {978-1-4665-9115-8},
    keywords = {dc, ee, greenlsi, josem},
    month = jan,
    pages = {267-286+},
    posted-at = {2015-08-07 08:48:02},
    priority = {2},
    publisher = {CRC Press},
    title = {{Energy-Aware} Policies in Ubiquitous Computing Facilities},
    url = {http://dx.doi.org/10.1201/b18021-13},
    year = {2015}
    }

  • I. Aransay, M. Zapater, P. Arroba, and J. M. Moya, “A trust and reputation system for energy optimization in cloud data centers,” in Ieee international conference on cloud computing (cloud), 2015. doi:10.1109/CLOUD.2015.28
    [BibTeX] [Abstract] [Download PDF]

    The increasing success of Cloud Computing applications and online services has contributed to the unsustainability of data center facilities in terms of energy consumption. Higher resource demand has increased the electricity required by computation and cooling resources, leading to power shortages and outages, specially in urban infrastructures. Current energy reduction strategies for Cloud facilities usually disregard the data center topology, the contribution of cooling consumption and the scalability of optimization strategies. Our work tackles the energy challenge by proposing a temperature-aware {VM} allocation policy based on a {Trust-and-Reputation} System ({TRS}). A {TRS} meets the requirements for inherently distributed environments such as data centers, and allows the implementation of autonomous and scalable {VM} allocation techniques. For this purpose, we model the relationships between the different computational entities, synthesizing this information in one single metric. This metric, called reputation, would be used to optimize the allocation of {VMs} in order to reduce energy consumption. We validate our approach with a state-of-the-art Cloud simulator using real Cloud traces. Our results show considerable reduction in energy consumption, reaching up to 46.16\% savings in computing power and 17.38\% savings in cooling, without {QoS} degradation while keeping servers below thermal redlining. Moreover, our results show the limitations of the {PUE} ratio as a metric for energy efficiency. To the best of our knowledge, this paper is the first approach in combining {Trust-and-Reputation} systems with Cloud Computing {VM} allocation.

    @inproceedings{AZAM15,
    abstract = {The increasing success of Cloud Computing applications and online services has contributed to the unsustainability of data center facilities in terms of energy consumption. Higher resource demand has increased the electricity required by computation and cooling resources, leading to power shortages and outages, specially in urban infrastructures. Current energy reduction strategies for Cloud facilities usually disregard the data center topology, the contribution of cooling consumption and the scalability of optimization strategies. Our work tackles the energy challenge by proposing a temperature-aware {VM} allocation policy based on a {Trust-and-Reputation} System ({TRS}). A {TRS} meets the requirements for inherently distributed environments such as data centers, and allows the implementation of autonomous and scalable {VM} allocation techniques. For this purpose, we model the relationships between the different computational entities, synthesizing this information in one single metric. This metric, called reputation, would be used to optimize the allocation of {VMs} in order to reduce energy consumption. We validate our approach with a state-of-the-art Cloud simulator using real Cloud traces. Our results show considerable reduction in energy consumption, reaching up to 46.16\% savings in computing power and 17.38\% savings in cooling, without {QoS} degradation while keeping servers below thermal redlining. Moreover, our results show the limitations of the {PUE} ratio as a metric for energy efficiency. To the best of our knowledge, this paper is the first approach in combining {Trust-and-Reputation} systems with Cloud Computing {VM} allocation.},
    author = {Aransay, Ignacio and Zapater, Marina and Arroba, Patricia and Moya, Jos\'{e} M.},
    booktitle = {IEEE International Conference on Cloud Computing (CLOUD)},
    citeulike-article-id = {13696426},
    citeulike-linkout-0 = {http://dx.doi.org/10.1109/CLOUD.2015.28},
    doi = {10.1109/CLOUD.2015.28},
    keywords = {cloud, ee, greenlsi, josem},
    posted-at = {2015-08-07 08:44:35},
    priority = {2},
    publisher = {IEEE},
    title = {A Trust and Reputation system for energy optimization in Cloud data centers},
    url = {http://dx.doi.org/10.1109/CLOUD.2015.28},
    year = {2015}
    }

  • J. C. Salinas-Hilburg, M. Zapater, J. L. Risco-Martín, J. M. Moya, and J. L. Ayala, “Using grammatical evolution techniques to model the dynamic power consumption of enterprise servers,” in International conference on complex, intelligent and software intensive systems, 2015. doi:10.1109/CISIS.2015.16
    [BibTeX] [Abstract] [Download PDF]

    The increasing demand for computational resources has led to a significant growth of data center facilities. A major concern has appeared regarding energy efficiency and consumption in servers and data centers. The use of flexible and scalable server power models is a must in order to enable proactive energy optimization strategies. This paper proposes the use of Evolutionary Computation to obtain a model for server dynamic power consumption. To accomplish this, we collect a significant number of server performance counters for a wide range of sequential and parallel applications, and obtain a model via Genetic Programming techniques. Our methodology enables the unsupervised generation of models for arbitrary server architectures, in a way that is robust to the type of application being executed in the server. With our generated models, we are able to predict the overall server power consumption for arbitrary workloads, outperforming previous approaches in the state-of-the-art.

    @inproceedings{SZR+15,
    abstract = {The increasing demand for computational resources has led to a significant growth of data center facilities. A major concern has appeared regarding energy efficiency and consumption in servers and data centers. The use of flexible and scalable server power models is a must in order to enable proactive energy optimization strategies. This paper proposes the use of Evolutionary Computation to obtain a model for server dynamic power consumption. To accomplish this, we collect a significant number of server performance counters for a wide range of sequential and parallel applications, and obtain a model via Genetic Programming techniques. Our methodology enables the unsupervised generation of models for arbitrary server architectures, in a way that is robust to the type of application being executed in the server. With our generated models, we are able to predict the overall server power consumption for arbitrary workloads, outperforming previous approaches in the state-of-the-art.},
    author = {Salinas-Hilburg, Juan C. and Zapater, Marina and Risco-Mart\'{\i}n, Jos\'{e} L. and Moya, Jos\'{e} M. and Ayala, Jos\'{e} L.},
    booktitle = {International Conference on Complex, Intelligent and Software Intensive Systems},
    citeulike-article-id = {13696427},
    citeulike-linkout-0 = {http://dx.doi.org/10.1109/CISIS.2015.16},
    doi = {10.1109/CISIS.2015.16},
    keywords = {ee, greenlsi, josem},
    posted-at = {2015-08-07 08:44:13},
    priority = {2},
    publisher = {IEEE},
    title = {Using Grammatical Evolution Techniques to Model the Dynamic Power Consumption of Enterprise Servers},
    url = {http://dx.doi.org/10.1109/CISIS.2015.16},
    year = {2015}
    }

  • M. J. Colmenar, A. Cuesta, Z. Bankovic, J. L. Risco-Martin, M. Zapater, J. I. Hidalgo, J. L. Ayala, and J. M. Moya, “Comparative study of meta-heuristic 3D floorplanning algorithms,” Neurocomputing, vol. 150, pp. 67-81, 2015. doi:10.1016/j.neucom.2014.06.078
    [BibTeX] [Abstract] [Download PDF]

    Constant necessity of improving performance has brought the invention of {3D} chips. The improvement is achieved due to the reduction of wire length, which results in decreased interconnection delay. However, {3D} stacks have less heat dissipation due to the inner layers, which leads to increased temperature and the appearance of hot spots. This problem can be mitigated through appropriate floorplanning. For this reason, in this work we present and compare five different solutions for floorplanning of {3D} chips. Each solution uses a different representation, and all are based on meta-heuristic algorithms, namely three of them are based on simulated annealing, while two other are based on evolutionary algorithms. The results show great capability of all the solutions in optimizing temperature and wire length, as they all exhibit significant improvements comparing to the benchmark floorplans.

    @article{CCB+14,
    abstract = {Constant necessity of improving performance has brought the invention of {3D} chips. The improvement is achieved due to the reduction of wire length, which results in decreased interconnection delay. However, {3D} stacks have less heat dissipation due to the inner layers, which leads to increased temperature and the appearance of hot spots. This problem can be mitigated through appropriate floorplanning. For this reason, in this work we present and compare five different solutions for floorplanning of {3D} chips. Each solution uses a different representation, and all are based on meta-heuristic algorithms, namely three of them are based on simulated annealing, while two other are based on evolutionary algorithms. The results show great capability of all the solutions in optimizing temperature and wire length, as they all exhibit significant improvements comparing to the benchmark floorplans.},
    author = {Colmenar, J. Manuel and Cuesta, Alfredo and Bankovic, Zorana and Risco-Martin, Jose L. and Zapater, Marina and Hidalgo, Jose I. and Ayala, Jose L. and Moya, Jose M.},
    citeulike-article-id = {13337914},
    citeulike-linkout-0 = {http://www.sciencedirect.com/science/article/pii/S0925231214012429},
    citeulike-linkout-1 = {http://dx.doi.org/10.1016/j.neucom.2014.06.078},
    doi = {10.1016/j.neucom.2014.06.078},
    journal = {Neurocomputing},
    keywords = {ee, floorplanning, greenlsi, josem},
    month = feb,
    pages = {67--81},
    posted-at = {2014-10-03 18:41:01},
    priority = {2},
    publisher = {Elsevier},
    title = {Comparative study of meta-heuristic {3D} floorplanning algorithms},
    url = {http://www.sciencedirect.com/science/article/pii/S0925231214012429},
    volume = {150},
    year = {2015}
    }

2014

  • P. Arroba, J. L. Risco-Martín, M. Zapater, J. M. Moya, and J. L. Ayala, “Enhancing regression models for complex systems using evolutionary techniques for feature engineering,” Journal of grid computing, 2014. doi:10.1007/s10723-014-9313-8
    [BibTeX] [Abstract] [Download PDF]

    This work proposes an automatic methodology for modeling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer’s expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimizes error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98 \%. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.

    @article{ARZ+14b,
    abstract = {This work proposes an automatic methodology for modeling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer's expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimizes error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98 \%. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.},
    author = {Arroba, Patricia and Risco-Mart\'{\i}n, Jos\'{e} L. and Zapater, Marina and Moya, Jos\'{e} M. and Ayala, Jos\'{e} L.},
    citeulike-article-id = {13379223},
    citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10723-014-9313-8},
    doi = {10.1007/s10723-014-9313-8},
    issn = {1570-7873},
    journal = {Journal of Grid Computing},
    keywords = {dc, greenlsi, josem},
    month = sep,
    posted-at = {2014-10-04 17:51:59},
    priority = {2},
    title = {Enhancing Regression Models for Complex Systems Using Evolutionary Techniques for Feature Engineering},
    url = {http://dx.doi.org/10.1007/s10723-014-9313-8},
    year = {2014}
    }

  • P. Arroba, J. L. Risco-Martín, M. Zapater, J. M. Moya, J. L. Ayala, and K. Olcoz, “Server power modeling for Run-Time energy optimization of cloud computing facilities,” in International conference on sustainability in energy and buildings, 2014. doi:10.1016/j.egypro.2014.12.402
    [BibTeX] [Download PDF]
    @inproceedings{ARZ+14,
    author = {Arroba, Patricia and Risco-Mart\'{\i}n, Jos\'{e} L. and Zapater, Marina and Moya, Jos\'{e} M. and Ayala, Jos\'{e} L. and Olcoz, Katzalin},
    booktitle = {International Conference on Sustainability in Energy and Buildings},
    citeulike-article-id = {13166637},
    citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.egypro.2014.12.402},
    doi = {10.1016/j.egypro.2014.12.402},
    keywords = {ee, greendisc, greenlsi, josem},
    posted-at = {2014-10-03 18:42:45},
    priority = {2},
    title = {Server Power Modeling for {Run-Time} Energy Optimization of Cloud Computing Facilities},
    url = {http://dx.doi.org/10.1016/j.egypro.2014.12.402},
    year = {2014}
    }

  • M. Zapater, J. L. Ayala, and J. M. Moya, “Proactive and reactive thermal aware optimization techniques to minimize the environmental impact of data centers,” in Design automation conference, 2014.
    [BibTeX]
    @inproceedings{ZAM14,
    author = {Zapater, Marina and Ayala, Jose L. and Moya, Jose M.},
    booktitle = {Design Automation Conference},
    citeulike-article-id = {13337901},
    keywords = {ee, greendisc, greenlsi, josem},
    posted-at = {2014-10-03 18:42:16},
    priority = {2},
    series = {DAC'14},
    title = {Proactive and Reactive Thermal Aware Optimization Techniques to Minimize the Environmental Impact of Data Centers},
    year = {2014}
    }

  • P. Arroba, J. L. Risco-Martín, M. Zapater, J. M. Moya, J. L. Ayala, and K. Olcoz, “Evolutionary power modeling for high-end servers in cloud data centers,” in Mathematical modelling in engineering & human behaviour, 2014.
    [BibTeX]
    @inproceedings{ARZ+14,
    author = {Arroba, Patricia and Risco-Mart\'{\i}n, Jose L. and Zapater, Marina and Moya, Jose M. and Ayala, Jose L. and Olcoz, Katzalin},
    booktitle = {Mathematical Modelling in Engineering \& Human Behaviour},
    citeulike-article-id = {13337906},
    keywords = {cloud, ee, greendisc, greenlsi, josem},
    posted-at = {2014-10-03 18:41:51},
    priority = {2},
    title = {Evolutionary Power Modeling for High-end Servers in Cloud Data Centers},
    year = {2014}
    }

  • M. Zapater, P. Arroba, J. L. Ayala, J. M. Moya, and K. Olcoz, “A novel energy-driven computing paradigm for e-health scenarios,” Future generation computer systems, vol. 34, pp. 138-154, 2014. doi:10.1016/j.future.2013.12.012
    [BibTeX] [Abstract] [Download PDF]

    A first-rate {e-Health} system saves lives, provides better patient care, allows complex but useful epidemiologic analysis and saves money. However, there may also be concerns about the costs and complexities associated with e-health implementation, and the need to solve issues about the energy footprint of the high-demanding computing facilities. This paper proposes a novel and evolved computing paradigm that: (i) provides the required computing and sensing resources; (ii) allows the population-wide diffusion; (iii) exploits the storage, communication and computing services provided by the Cloud; (iv) tackles the energy-optimization issue as a first-class requirement, taking it into account during the whole development cycle. The novel computing concept, and the multi-layer top-down energy-optimization methodology, obtain promising results in a realistic scenario for cardiovascular tracking and analysis, making the Home Assisted Living a reality.

    @article{ZAA+13,
    abstract = {A first-rate {e-Health} system saves lives, provides better patient care, allows complex but useful epidemiologic analysis and saves money. However, there may also be concerns about the costs and complexities associated with e-health implementation, and the need to solve issues about the energy footprint of the high-demanding computing facilities. This paper proposes a novel and evolved computing paradigm that: (i) provides the required computing and sensing resources; (ii) allows the population-wide diffusion; (iii) exploits the storage, communication and computing services provided by the Cloud; (iv) tackles the energy-optimization issue as a first-class requirement, taking it into account during the whole development cycle. The novel computing concept, and the multi-layer top-down energy-optimization methodology, obtain promising results in a realistic scenario for cardiovascular tracking and analysis, making the Home Assisted Living a reality.},
    author = {Zapater, Marina and Arroba, Patricia and Ayala, Jos\'{e} L. and Moya, Jos\'{e} M. and Olcoz, Katzalin},
    citeulike-article-id = {12882215},
    citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.future.2013.12.012},
    citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/pii/S0167739X13002768},
    doi = {10.1016/j.future.2013.12.012},
    howpublished = {online},
    issn = {0167-739X},
    journal = {Future Generation Computer Systems},
    keywords = {ami, analysis, centers, cloud, computing, data, dc, efficiency, energy, green, greenlsi, it, josem, management, population, resource},
    month = may,
    pages = {138--154},
    posted-at = {2013-12-26 17:29:36},
    priority = {0},
    title = {A novel energy-driven computing paradigm for e-health scenarios},
    url = {http://www.sciencedirect.com/science/article/pii/S0167739X13002768},
    volume = {34},
    year = {2014}
    }

2013

  • P. Malagón, J. M. Goyeneche, and J. M. Moya, “Exploiting parallelism opportunities in non-parallel architectures to improve NLFSR software implementations,” in Xxviii conference on design of circuits and integrated systems, 2013, pp. 454-460.
    [BibTeX] [Abstract]

    Linear and {Non-Linear} Feedback Shift Registers ({LFSR} and {NLFSR}) are widely used in the generation of pseudorandom sequences and as basic structures of block ciphers. The algorithms are structured in rounds where a new bit is calculated and shifted into the keystream or data word. They can be implemented as a set of binary operations over a set of bits belonging to a register and a bit-wise rotation performed in each round. Although these algorithms are more efficiently implemented in hardware, when run in software the binary operations are usually performed in an {ALU}, calculating only 1 bit at a time. But {ALUs} have the potential of performing more Boolean operations simultaneously. We propose to consider the {ALU} as a vector unit of Boolean {ALUs}. Therefore, we include a methodology to exploit the parallelization of the Boolean operations in the {ALU} when possible in order to improve the performance of software implementations. The optimization is susceptible to be incorporated and automatically applied by a compiler. Using {KeeLoq} as use case in a 16-bit platform we obtain an improvement factor in executed cycles of at least 2.45, increasing code size in bytes by at most 2.27.

    @inproceedings{MGM13,
    abstract = {Linear and {Non-Linear} Feedback Shift Registers ({LFSR} and {NLFSR}) are widely used in the generation of pseudorandom sequences and as basic structures of block ciphers. The algorithms are structured in rounds where a new bit is calculated and shifted into the keystream or data word. They can be implemented as a set of binary operations over a set of bits belonging to a register and a bit-wise rotation performed in each round. Although these algorithms are more efficiently implemented in hardware, when run in software the binary operations are usually performed in an {ALU}, calculating only 1 bit at a time. But {ALUs} have the potential of performing more Boolean operations simultaneously. We propose to consider the {ALU} as a vector unit of Boolean {ALUs}. Therefore, we include a methodology to exploit the parallelization of the Boolean operations in the {ALU} when possible in order to improve the performance of software implementations. The optimization is susceptible to be incorporated and automatically applied by a compiler. Using {KeeLoq} as use case in a 16-bit platform we obtain an improvement factor in executed cycles of at least 2.45, increasing code size in bytes by at most 2.27.},
    author = {Malag\'{o}n, P. and Goyeneche, J. M. and Moya, J. M.},
    booktitle = {XXVIII Conference on Design of Circuits and Integrated Systems},
    citeulike-article-id = {13581791},
    isbn = {978-84-8081-401-0},
    keywords = {bitslice, greenlsi, josem, nlfsr, sca},
    month = nov,
    pages = {454--460},
    posted-at = {2015-11-13 23:46:13},
    priority = {2},
    title = {Exploiting parallelism opportunities in non-parallel architectures to improve {NLFSR} software implementations},
    year = {2013}
    }

  • J. Pagán, M. Zapater, Ó. Cubo, P. Arroba, V. Martín, and J. M. Moya, “A Cyber-Physical approach to combined HW-SW monitoring for improving energy efficiency in data centers,” in Conference on design of circuits and integrated systems, 2013.
    [BibTeX] [Abstract]

    {High-Performance} Computing, Cloud computing and next-generation applications such {e-Health} or Smart Cities have dramatically increased the computational demand of Data Centers. The huge energy consumption, increasing levels of {CO2} and the economic costs of these facilities represent a challenge for industry and researchers alike. Recent research trends propose the usage of holistic optimization techniques to jointly minimize Data Center computational and cooling costs from a multilevel perspective. This paper presents an analysis on the parameters needed to integrate the Data Center in a holistic optimization framework and leverages the usage of {Cyber-Physical} systems to gather workload, server and environmental data via software techniques and by deploying a non-intrusive Wireless Sensor Network ({WSN}). This solution tackles data sampling, retrieval and storage from a reconfigurable perspective, reducing the amount of data generated for optimization by a 68\% without information loss, doubling the lifetime of the {WSN} nodes and allowing runtime energy minimization techniques in a real scenario.

    @inproceedings{PZC+13,
    abstract = {{High-Performance} Computing, Cloud computing and next-generation applications such {e-Health} or Smart Cities have dramatically increased the computational demand of Data Centers. The huge energy consumption, increasing levels of {CO2} and the economic costs of these facilities represent a challenge for industry and researchers alike. Recent research trends propose the usage of holistic optimization techniques to jointly minimize Data Center computational and cooling costs from a multilevel perspective. This paper presents an analysis on the parameters
    needed to integrate the Data Center in a holistic optimization framework and leverages the usage of {Cyber-Physical} systems to gather workload, server and environmental data via software techniques and by deploying a non-intrusive Wireless Sensor Network ({WSN}). This solution tackles data sampling, retrieval and storage from a reconfigurable perspective, reducing the amount of data generated for optimization by a 68\% without information loss, doubling the lifetime of the {WSN} nodes and allowing runtime energy minimization techniques in a real scenario.},
    author = {Pag\'{a}n, Josu\'{e} and Zapater, Marina and Cubo, \'{O}scar and Arroba, Patricia and Mart\'{\i}n, Vicente and Moya, Jos\'{e} M.},
    booktitle = {Conference on Design of Circuits and Integrated Systems},
    citeulike-article-id = {12651885},
    keywords = {cps, dc, greenlsi, josem, wsn},
    month = nov,
    posted-at = {2014-03-01 09:08:45},
    priority = {2},
    title = {A {Cyber-Physical} Approach to Combined {HW}-{SW} Monitoring for Improving Energy Efficiency in Data Centers},
    year = {2013}
    }

  • P. Arroba, M. Zapater, J. L. Ayala, J. M. Moya, K. Olcoz, and R. Hermida, “On the Leakage-Power modeling for optimal server operation,” in Jornadas sarteco, 2013.
    [BibTeX] [Abstract] [Download PDF]

    Leakage power consumption is a component of the total power consumption in data centers that is not traditionally considered in the set-point temperature of the room. However, the effect of this power component, increased with temperature, can determine the savings associated with the careful management of the cooling system, as well as the reliability of the system. The work presented in this paper detects the need of addressing leakage power in order to achieve substantial savings in the energy consumption of servers. In particular, our work shows that, by a careful detection and management of two working regions (low and high impact of thermal-dependent leakage), energy consumption of the datacenter can be optimized by a reduction of the cooling budget.

    @inproceedings{AZA+13,
    abstract = {Leakage power consumption is a component of the total power consumption in data centers that is not traditionally considered in the set-point temperature of the room. However, the effect of this power component, increased with temperature, can determine the savings associated with the careful management of the cooling system, as well as the reliability of the system. The work presented in this paper detects the need of addressing leakage power in order to achieve substantial savings in the energy
    consumption of servers. In particular, our work shows that, by a careful detection and management of two
    working regions (low and high impact of thermal-dependent leakage), energy consumption of the datacenter can be optimized by a reduction of the cooling
    budget.},
    author = {Arroba, Patricia and Zapater, Marina and Ayala, Jos\'{e} L. and Moya, Jos\'{e} M. and Olcoz, Katzalin and Hermida, Rom\'{a}n},
    booktitle = {Jornadas SARTECO},
    citeulike-article-id = {12651866},
    citeulike-linkout-0 = {http://www.sc.ehu.es/acwmialj/papers/2013\_jornadas.pdf},
    keywords = {greendisc, greenlsi, josem, modeling},
    month = sep,
    posted-at = {2014-03-01 09:08:13},
    priority = {2},
    title = {On the {Leakage-Power} Modeling for Optimal Server Operation},
    url = {http://www.sc.ehu.es/acwmialj/papers/2013\_jornadas.pdf},
    year = {2013}
    }

  • Z. Bankovic, J. C. Vallejo, D. Fraga, and J. M. Moya, “Detecting false testimonies in reputation systems using self-organizing maps,” Logic journal of the igpl, vol. 21, iss. 4, pp. 549-559, 2013. doi:10.1093/jigpal/jzs028
    [BibTeX] [Abstract] [Download PDF]

    It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario, the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack. In another scenario, a number of entities agree to give positive feedback on an entity (often with adversarial intentions). This attack is known as ballot stuffing. Both attack types can significantly deteriorate the performances of the network. The existing solutions for coping with these attacks are mainly concentrated on prevention techniques. In this work, we propose a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrate the capability of the approach in detecting both bad mouthing and ballot stuffing attack in various scenarios.

    @article{BVFM13,
    abstract = {It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario, the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack. In another scenario, a number of entities agree to give positive feedback on an entity (often with adversarial intentions). This attack is known as ballot stuffing. Both attack types can significantly deteriorate the performances of the network. The existing solutions for coping with these attacks are mainly concentrated on prevention techniques. In this work, we propose a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrate the capability of the approach in detecting both bad mouthing and ballot stuffing attack in various scenarios.},
    author = {Bankovic, Zorana and Vallejo, Juan C. and Fraga, David and Moya, Jos\'{e} M.},
    citeulike-article-id = {12799817},
    citeulike-linkout-0 = {http://dx.doi.org/10.1093/jigpal/jzs028},
    doi = {10.1093/jigpal/jzs028},
    journal = {Logic Journal of the IGPL},
    keywords = {ami, greenlsi, josem, repsys},
    number = {4},
    pages = {549--559},
    posted-at = {2013-11-25 08:51:40},
    priority = {0},
    title = {Detecting false testimonies in reputation systems using self-organizing maps},
    url = {http://dx.doi.org/10.1093/jigpal/jzs028},
    volume = {21},
    year = {2013}
    }

  • M. Zapater, J. L. Ayala, J. M. Moya, K. Vaidyanathan, K. Gross, and A. K. Coskun, “Leakage and temperature aware server control for improving energy efficiency in data centers,” in Proceedings of the conference on design, automation and test in europe, 2013, pp. 266-269.
    [BibTeX] [Abstract] [Download PDF]

    Reducing the energy consumption for computation and cooling in servers is a major challenge considering the data center energy costs today. To ensure energy-efficient operation of servers in data centers, the relationship among computational power, temperature, leakage, and cooling power needs to be analyzed. By means of an innovative setup that enables monitoring and controlling the computing and cooling power consumption separately on a commercial enterprise server, this paper studies temperature-leakage-energy tradeoffs, obtaining an empirical model for the leakage component. Using this model, we design a controller that continuously seeks and settles at the optimal fan speed to minimize the energy consumption for a given workload. We run a customized dynamic load-synthesis tool to stress the system. Our proposed cooling controller achieves up to 9\% energy savings and {30W} reduction in peak power in comparison to the default cooling control scheme.

    @inproceedings{ZAM+13,
    abstract = {Reducing the energy consumption for computation and cooling in servers is a major challenge considering the data center energy costs today. To ensure energy-efficient operation of servers in data centers, the relationship among computational power, temperature, leakage, and cooling power needs to be analyzed. By means of an innovative setup that enables
    monitoring and controlling the computing and cooling power consumption separately on a commercial enterprise server, this paper studies temperature-leakage-energy tradeoffs, obtaining an empirical model for the leakage component. Using this model, we design a controller that continuously seeks and settles at the
    optimal fan speed to minimize the energy consumption for a given workload. We run a customized dynamic load-synthesis tool to stress the system. Our proposed cooling controller achieves up to 9\% energy savings and {30W} reduction in peak power in comparison to the default cooling control scheme.},
    author = {Zapater, Marina and Ayala, Jos\'{e} L. and Moya, Jos\'{e} M. and Vaidyanathan, Kalyan and Gross, Kenny and Coskun, Ayse K.},
    booktitle = {Proceedings of the Conference on Design, Automation and Test in Europe},
    citeulike-article-id = {12231242},
    citeulike-linkout-0 = {http://dl.acm.org/citation.cfm?id=2485354},
    isbn = {978-1-4503-2153-2},
    keywords = {cooling, dc, greenlsi, josem},
    pages = {266--269},
    posted-at = {2013-04-06 08:39:15},
    priority = {2},
    series = {DATE '13},
    title = {Leakage and Temperature Aware Server Control for Improving Energy Efficiency in Data Centers},
    url = {http://dl.acm.org/citation.cfm?id=2485354},
    year = {2013}
    }

  • Z. Banković, D. Fraga, J. M. Moya, J. C. Vallejo, P. Malagón, Á. Araujo, J. de Goyeneche, E. Romero, J. Blesa, D. Villanueva, and O. Nieto-Taladriz, “Bio-inspired enhancement of reputation systems for intelligent environments,” Information sciences, vol. 222, pp. 99-112, 2013. doi:10.1016/j.ins.2011.07.032
    [BibTeX] [Abstract] [Download PDF]

    Providing security to the emerging field of ambient intelligence will be difficult if we rely only on existing techniques, given their dynamic and heterogeneous nature. Moreover, security demands of these systems are expected to grow, as many applications will require accurate context modeling. In this work we propose an enhancement to the reputation systems traditionally deployed for securing these systems. Different anomaly detectors are combined using the immunological paradigm to optimize reputation system performance in response to evolving security requirements. As an example, the experiments show how a combination of detectors based on unsupervised techniques (self-organizing maps and genetic algorithms) can help to significantly reduce the global response time of the reputation system. The proposed solution offers many benefits: scalability, fast response to adversarial activities, ability to detect unknown attacks, high adaptability, and high ability in detecting and confining attacks. For these reasons, we believe that our solution is capable of coping with the dynamism of ambient intelligence systems and the growing requirements of security demands.

    @article{BFM+13,
    abstract = {Providing security to the emerging field of ambient intelligence will be difficult if we rely only on existing techniques, given their dynamic and heterogeneous nature. Moreover, security demands of these systems are expected to grow, as many applications will require accurate context modeling. In this work we propose an enhancement to the reputation systems traditionally deployed for securing these systems. Different anomaly detectors are combined using the immunological paradigm to optimize reputation system performance in response to evolving security requirements. As an example, the experiments show how a combination of detectors based on unsupervised techniques (self-organizing maps and genetic algorithms) can help to significantly reduce the global response time of the reputation system. The proposed solution offers many benefits: scalability, fast response to adversarial activities, ability to detect unknown attacks, high adaptability, and high ability in detecting and confining attacks. For these reasons, we believe that our solution is capable of coping with the dynamism of ambient intelligence systems and the growing requirements of security demands.},
    author = {Bankovi\'{c}, Zorana and Fraga, David and Moya, Jos\'{e} M. and Vallejo, Juan C. and Malag\'{o}n, Pedro and Araujo, \'{A}lvaro and de Goyeneche, Juan-Mariano and Romero, Elena and Blesa, Javier and Villanueva, Daniel and Nieto-Taladriz, Octavio},
    citeulike-article-id = {9654296},
    citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.ins.2011.07.032},
    doi = {10.1016/j.ins.2011.07.032},
    issn = {00200255},
    journal = {Information Sciences},
    keywords = {greenlsi, josem},
    month = feb,
    pages = {99--112},
    posted-at = {2012-11-18 18:39:33},
    priority = {2},
    publisher = {Elsevier},
    title = {Bio-inspired enhancement of reputation systems for intelligent environments},
    url = {http://dx.doi.org/10.1016/j.ins.2011.07.032},
    volume = {222},
    year = {2013}
    }

2012

  • Z. Banković, D. Fraga, J. Vallejo, and J. Moya, “Self-Organizing maps versus growing neural gas in detecting data outliers for security applications,” in Hybrid artificial intelligent systems, E. Corchado, V. Snášel, A. Abraham, M. Woźniak, M. Graña, and S. Cho, Eds., Springer Berlin Heidelberg, 2012, vol. 7209, pp. 89-96. doi:10.1007/978-3-642-28931-6_9
    [BibTeX] [Abstract] [Download PDF]

    Our previous work has demonstrated that clustering-based outlier detection approach offers numerous advantages for detecting attacks in Wireless Sensor Networks, above all adaptability and the possibility to detect unknown attacks. In this work we provide a comparison of Self-organizing maps ({SOM}) and Growing Neural Gas ({GNG}) used for this purpose. Our results reveal that {GNG} is superior to {SOM} when it comes to the level of presence of anomalous data during the training, as {GNG} is capable of detecting the attack even with small portion of normal data during the training, while {SOM} need the majority of the training data to be normal in order to detect it. On the other hand, after both being trained with normal data, {SOM} performs somewhat better as the attack becomes more aggressive, i.e. it exhibits higher detection rate, although both are capable of detecting the attack in each case.

    @incollection{BFVM12,
    abstract = {Our previous work has demonstrated that clustering-based outlier detection approach offers numerous advantages for detecting attacks in Wireless Sensor Networks, above all adaptability and the possibility to detect unknown attacks. In this work we provide a comparison of Self-organizing maps ({SOM}) and Growing Neural Gas ({GNG}) used for this purpose. Our results reveal that {GNG} is superior to {SOM} when it comes to the level of presence of anomalous data during the training, as {GNG} is capable of detecting the attack even with small portion of normal data during the training, while {SOM} need the majority of the training data to be normal in order to detect it. On the other hand, after both being trained with normal data, {SOM} performs somewhat better as the attack becomes more aggressive, i.e. it exhibits higher detection rate, although both are capable of detecting the attack in each case.},
    author = {Bankovi\'{c}, Zorana and Fraga, David and Vallejo, JuanCarlos and Moya, Jos\'{e}M},
    booktitle = {Hybrid Artificial Intelligent Systems},
    citeulike-article-id = {13942918},
    citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-28931-6\_9},
    citeulike-linkout-1 = {http://link.springer.com/chapter/10.1007/978-3-642-28931-6\_9},
    doi = {10.1007/978-3-642-28931-6\_9},
    editor = {Corchado, Emilio and Sn\'{a}\v{s}el, V\'{a}clav and Abraham, Ajith and Wo\'{z}niak, Micha{\l} and Gra\~{n}a, Manuel and Cho, Sung-Bae},
    keywords = {dfraga, greenlsi, josem, repsys, security},
    pages = {89--96},
    posted-at = {2016-02-27 12:03:06},
    priority = {2},
    publisher = {Springer Berlin Heidelberg},
    series = {Lecture Notes in Computer Science},
    title = {{Self-Organizing} Maps versus Growing Neural Gas in Detecting Data Outliers for Security Applications},
    url = {http://dx.doi.org/10.1007/978-3-642-28931-6\_9},
    volume = {7209},
    year = {2012}
    }

  • D. Fraga, Z. Bankovic, and J. M. Moya, “A taxonomy of trust and reputation system attacks,” in Trust, security and privacy in computing and communications (trustcom), 2012 ieee 11th international conference on, 2012, pp. 41-50. doi:10.1109/trustcom.2012.58
    [BibTeX] [Abstract] [Download PDF]

    Trust and reputation have been suggested as an effective security mechanism for open and distributed environments. However, even though there exists a high number of identified attacks against Trust and Reputation Systems ({TRS}), a generic security framework to identify all of them in a holistic way has not yet been proposed. This work presents a {TRS} attack taxonomy based on an {TRS} architectural model and a set of well-known security topics. Based on this taxonomy, two main deficiencies of the state-of-the-art in {TRS} security literature are detected: the existence of {TRS} attacks that have received few attention from the {TRS} community despite its importance, and potential threats that have not been previously identified. Finally, a real-life {TRS} scenario is analyzed as a proof-of-concept of the proposed taxonomy.

    @inproceedings{FBM12,
    abstract = {Trust and reputation have been suggested as an effective security mechanism for open and distributed environments. However, even though there exists a high number of identified attacks against Trust and Reputation Systems ({TRS}), a generic security framework to identify all of them in a holistic way has not yet been proposed. This work presents a {TRS} attack taxonomy based on an {TRS} architectural model and a set of well-known security topics. Based on this taxonomy, two main deficiencies of the state-of-the-art in {TRS} security literature are detected: the existence of {TRS} attacks that have received few attention from the {TRS} community despite its importance, and potential threats that have not been previously identified. Finally, a real-life {TRS} scenario is analyzed as a proof-of-concept of the proposed taxonomy.},
    author = {Fraga, D. and Bankovic, Z. and Moya, J. M.},
    booktitle = {Trust, Security and Privacy in Computing and Communications (TrustCom), 2012 IEEE 11th International Conference on},
    citeulike-article-id = {13942896},
    citeulike-linkout-0 = {http://dx.doi.org/10.1109/trustcom.2012.58},
    citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=6295956},
    doi = {10.1109/trustcom.2012.58},
    institution = {Dept. Of Electr. Eng. ETSI Telecomun., Univ. Politec. de Madrid, Madrid, Spain},
    isbn = {978-1-4673-2172-3},
    keywords = {dfraga, greenlsi, josem, repsys, security},
    month = jun,
    pages = {41--50},
    posted-at = {2016-02-27 11:34:19},
    priority = {2},
    publisher = {IEEE},
    title = {A Taxonomy of Trust and Reputation System Attacks},
    url = {http://dx.doi.org/10.1109/trustcom.2012.58},
    year = {2012}
    }

  • M. Zapater, J. L. Ayala, and J. M. Moya, “GreenDisc: a HW/SW energy optimization framework in globally distributed computation,” in Ubiquitous computing and ambient intelligence, J. Bravo, D. López-de Ipiña, and F. Moya, Eds., Springer Berlin Heidelberg, 2012, pp. 1-8. doi:10.1007/978-3-642-35377-2_1
    [BibTeX] [Abstract] [Download PDF]

    In recent future, wireless sensor networks ({WSNs}) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of {WSNs} facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers ({DCs}). The high economical and environmental impact of the energy consumption in {DCs} requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed. In this context, this paper shows the following on-going research lines and obtained results. In the field of {WSNs}: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of {DCs}: energy-optimal workload assignment policies in heterogeneous {DCs}, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the {DCs} that process the data provided by the {WSNs}.

    @incollection{ZAM12b,
    abstract = {In recent future, wireless sensor networks ({WSNs}) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of {WSNs} facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers ({DCs}). The high economical and environmental impact of the energy consumption in {DCs} requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed.
    In this context, this paper shows the following on-going research lines and obtained results. In the field of {WSNs}: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of {DCs}: energy-optimal workload assignment policies in heterogeneous {DCs}, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the {DCs} that process the data provided by the {WSNs}.},
    author = {Zapater, Marina and Ayala, Jos\'{e} L. and Moya, Jose M.},
    booktitle = {Ubiquitous Computing and Ambient Intelligence},
    citeulike-article-id = {11824300},
    citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-35377-2\_1},
    doi = {10.1007/978-3-642-35377-2\_1},
    editor = {Bravo, Jos\'{e} and L\'{o}pez-de Ipi\~{n}a, Diego and Moya, Francisco},
    keywords = {ami, greenlsi, josem},
    pages = {1--8},
    posted-at = {2012-12-04 09:00:30},
    priority = {2},
    publisher = {Springer Berlin Heidelberg},
    series = {Lecture Notes in Computer Science},
    title = {{GreenDisc}: A {HW}/{SW} Energy Optimization Framework in Globally Distributed Computation},
    url = {http://dx.doi.org/10.1007/978-3-642-35377-2\_1},
    year = {2012}
    }

  • M. Zapater, J. L. Ayala, and J. M. Moya, “Leveraging heterogeneity for energy minimization in data centers,” in Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID 2012), Washington, DC, USA, 2012. doi:10.1109/CCGrid.2012.34
    [BibTeX] [Abstract] [Download PDF]

    Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research.

    @inproceedings{ZAM12,
    abstract = {Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research.},
    address = {Washington, DC, USA},
    author = {Zapater, Marina and Ayala, Jose L. and Moya, Jose M.},
    booktitle = {{Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID 2012)}},
    citeulike-article-id = {11736953},
    citeulike-linkout-0 = {http://dx.doi.org/10.1109/CCGrid.2012.34},
    doi = {10.1109/CCGrid.2012.34},
    keywords = {centers, computing, data, energy-aware, green, greenlsi, heterogeneous, josem, optimization},
    posted-at = {2012-11-21 00:02:39},
    priority = {0},
    publisher = {IEEE Computer Society},
    series = {CCGRID '12},
    title = {Leveraging Heterogeneity for Energy Minimization in Data Centers},
    url = {http://dx.doi.org/10.1109/CCGrid.2012.34},
    year = {2012}
    }

  • Z. Banković, D. Fraga, J. M. Moya, and J. C. Vallejo, “Detecting unknown attacks in wireless sensor networks that contain mobile nodes,” Sensors, vol. 12, iss. 8, pp. 10834-10850, 2012. doi:10.3390/s120810834
    [BibTeX] [Abstract] [Download PDF]

    As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.

    @article{BFM+12,
    abstract = {As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.},
    author = {Bankovi\'{c}, Zorana and Fraga, David and Moya, Jos\'{e} M. and Vallejo, Juan C.},
    citeulike-article-id = {11095233},
    citeulike-linkout-0 = {http://dx.doi.org/10.3390/s120810834},
    day = {07},
    doi = {10.3390/s120810834},
    issn = {1424-8220},
    journal = {Sensors},
    keywords = {greenlsi, josem},
    month = aug,
    number = {8},
    pages = {10834--10850},
    posted-at = {2012-11-18 18:39:40},
    priority = {2},
    title = {Detecting Unknown Attacks in Wireless Sensor Networks That Contain Mobile Nodes},
    url = {http://dx.doi.org/10.3390/s120810834},
    volume = {12},
    year = {2012}
    }

  • M. Zapater, C. Sanchez, J. L. Ayala, J. M. Moya, and J. L. Risco-Martín, “Ubiquitous green computing techniques for high demand applications in smart environments,” Sensors, vol. 12, iss. 8, pp. 10659-10677, 2012. doi:10.3390/s120810659
    [BibTeX] [Abstract] [Download PDF]

    Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in {WSNs} infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the {WSN} infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.

    @article{ZSA+12,
    abstract = {Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in {WSNs} infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the {WSN} infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.},
    author = {Zapater, Marina and Sanchez, Cesar and Ayala, Jose L. and Moya, Jose M. and Risco-Mart\'{\i}n, Jos\'{e} L.},
    citeulike-article-id = {11021307},
    citeulike-linkout-0 = {http://dx.doi.org/10.3390/s120810659},
    day = {03},
    doi = {10.3390/s120810659},
    issn = {1424-8220},
    journal = {Sensors},
    keywords = {greenlsi, josem},
    month = aug,
    number = {8},
    pages = {10659--10677},
    posted-at = {2012-11-18 18:39:26},
    priority = {2},
    title = {Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments},
    url = {http://dx.doi.org/10.3390/s120810659},
    volume = {12},
    year = {2012}
    }

  • P. Malagón, J. de Goyeneche, M. Zapater, J. M. Moya, and Z. Banković, “Compiler optimizations as a countermeasure against Side-Channel analysis in MSP430-based devices,” Sensors, vol. 12, iss. 6, pp. 7994-8012, 2012. doi:10.3390/s120607994
    [BibTeX] [Abstract] [Download PDF]

    Ambient Intelligence ({AmI}) requires devices everywhere, dynamic and massively distributed networks of low-cost nodes that, among other data, manage private information or control restricted operations. {MSP430}, a 16-bit microcontroller, is used in {WSN} platforms, as the {TelosB}. Physical access to devices cannot be restricted, so attackers consider them a target of their malicious attacks in order to obtain access to the network. Side-channel analysis ({SCA}) easily exploits leakages from the execution of encryption algorithms that are dependent on critical data to guess the key value. In this paper we present an evaluation framework that facilitates the analysis of the effects of compiler and backend optimizations on the resistance against statistical {SCA}. We propose an optimization-based software countermeasure that can be used in current low-cost devices to radically increase resistance against statistical {SCA}, analyzed with the new framework.

    @article{MGZ+12,
    abstract = {Ambient Intelligence ({AmI}) requires devices everywhere, dynamic and massively distributed networks of low-cost nodes that, among other data, manage private information or control restricted operations. {MSP430}, a 16-bit microcontroller, is used in {WSN} platforms, as the {TelosB}. Physical access to devices cannot be restricted, so attackers consider them a target of their malicious attacks in order to obtain access to the network. Side-channel analysis ({SCA}) easily exploits leakages from the execution of encryption algorithms that are dependent on critical data to guess the key value. In this paper we present an evaluation framework that facilitates the analysis of the effects of compiler and backend optimizations on the resistance against statistical {SCA}. We propose an optimization-based software countermeasure that can be used in current low-cost devices to radically increase resistance against statistical {SCA}, analyzed with the new framework.},
    author = {Malag\'{o}n, Pedro and de Goyeneche, Juan-Mariano and Zapater, Marina and Moya, Jos\'{e} M. and Bankovi\'{c}, Zorana},
    citeulike-article-id = {10853003},
    citeulike-linkout-0 = {http://dx.doi.org/10.3390/s120607994},
    day = {08},
    doi = {10.3390/s120607994},
    issn = {1424-8220},
    journal = {Sensors},
    keywords = {greenlsi, josem},
    month = jun,
    number = {6},
    pages = {7994--8012},
    posted-at = {2012-11-18 18:39:16},
    priority = {2},
    title = {Compiler Optimizations as a Countermeasure against {Side-Channel} Analysis in {MSP430}-Based Devices},
    url = {http://dx.doi.org/10.3390/s120607994},
    volume = {12},
    year = {2012}
    }

2011

  • Z. Bankovic, D. Fraga, J. C. Vallejo, and J. M. Moya, “Improving reputation systems for wireless sensor networks using genetic algorithms,” in Proceedings of the 13th annual conference on genetic and evolutionary computation, New York, NY, USA, 2011, pp. 1643-1650. doi:10.1145/2001576.2001798
    [BibTeX] [Abstract] [Download PDF]

    In this article we propose to couple reputation systems for wireless sensor networks with a genetic algorithm in order to improve their time of response to adversarial activities. The reputation of each node is assigned by an unsupervised genetic algorithm trained for detecting outliers in the data. The response of the system consists in assigning low reputation values to the compromised nodes cutting them off from the rest of the network. The genetic algorithm uses the feature extraction process that does not capture the properties of the attacks, but rather relies on the existing temporal and spatial redundancy in sensor networks and tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. This solution offers many benefits: scalable solution, fast response to thwart activities, ability to detect unknown attacks, high adaptability, and high ability in detecting and confining attacks. Comparing to the standard clustering algorithms, the benefit of this one is that it is not necessary to assign the number of clusters from the beginning. The solution is also robust to both parameter changes and the presence of large amounts of malicious data in the training and testing datasets.

    @inproceedings{BFVM11,
    abstract = {In this article we propose to couple reputation systems for wireless sensor networks with a genetic algorithm in order to improve their time of response to adversarial activities. The reputation of each node is assigned by an unsupervised genetic algorithm trained for detecting outliers in the data. The response of the system consists in assigning low reputation values to the compromised nodes cutting them off from the rest of the network. The genetic algorithm uses the feature extraction process that does not capture the properties of the attacks, but rather relies on the existing temporal and spatial redundancy in sensor networks and tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. This solution offers many benefits: scalable solution, fast response to thwart activities, ability to detect unknown attacks, high adaptability, and high ability in detecting and confining attacks. Comparing to the standard clustering algorithms, the benefit of this one is that it is not necessary to assign the number of clusters from the beginning. The solution is also robust to both parameter changes and the presence of large amounts of malicious data in the training and testing datasets.},
    address = {New York, NY, USA},
    author = {Bankovic, Zorana and Fraga, David and Vallejo, Juan C. and Moya, Jos{\'{e}} M.},
    booktitle = {Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation},
    citeulike-article-id = {13942917},
    citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2001576.2001798},
    citeulike-linkout-1 = {http://dx.doi.org/10.1145/2001576.2001798},
    doi = {10.1145/2001576.2001798},
    isbn = {978-1-4503-0557-0},
    keywords = {dfraga, greenlsi, josem, repsys},
    location = {Dublin, Ireland},
    pages = {1643--1650},
    posted-at = {2016-02-27 11:59:03},
    priority = {2},
    publisher = {ACM},
    series = {GECCO '11},
    title = {Improving Reputation Systems for Wireless Sensor Networks Using Genetic Algorithms},
    url = {http://dx.doi.org/10.1145/2001576.2001798},
    year = {2011}
    }

  • Z. Bankovic, J. M. Moya, J. C. Vallejo, D. Fraga, and P. Malagon, “Holistic solution for confining insider attacks in wireless sensor networks using reputation systems coupled with clustering techniques,” in Trust, security and privacy in computing and communications (trustcom), 2011 ieee 10th international conference on, 2011, pp. 61-72. doi:10.1109/trustcom.2011.12
    [BibTeX] [Abstract] [Download PDF]

    The most serious obstacle in further proliferation of wireless sensor networks is their low level of security, where the insider attacks are the most challenging issue. In this work we propose a holistic solution for detecting and confining insider attacks that couples reputation systems with clustering techniques, namely unsupervised genetic algorithm and self-organizing maps, trained for detecting outliers in data. The novelty of this work is the redundancy in detecting agents, their evaluation based on the majority voting and the calculation of the reputation as the average value, which makes it more robust to different attack scenarios and their parameter variations. The algorithms use the feature space based on sequences of sensor outputs (both temporal and spatial), as well as the routing paths used to forward the data to the base station, and designed with the idea of introducing the ability to detect a wide range of attacks. The solution performs both attack detection and recovery from attacks, and it offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability and high ability in detecting and confining attacks.

    @inproceedings{BMV+11,
    abstract = {The most serious obstacle in further proliferation of wireless sensor networks is their low level of security, where the insider attacks are the most challenging issue. In this work we propose a holistic solution for detecting and confining insider attacks that couples reputation systems with clustering techniques, namely unsupervised genetic algorithm and self-organizing maps, trained for detecting outliers in data. The novelty of this work is the redundancy in detecting agents, their evaluation based on the majority voting and the calculation of the reputation as the average value, which makes it more robust to different attack scenarios and their parameter variations. The algorithms use the feature space based on sequences of sensor outputs (both temporal and spatial), as well as the routing paths used to forward the data to the base station, and designed with the idea of introducing the ability to detect a wide range of attacks. The solution performs both attack detection and recovery from attacks, and it offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability and high ability in detecting and confining attacks.},
    author = {Bankovic, Z. and Moya, J. M. and Vallejo, J. C. and Fraga, D. and Malagon, P.},
    booktitle = {Trust, Security and Privacy in Computing and Communications (TrustCom), 2011 IEEE 10th International Conference on},
    citeulike-article-id = {13942902},
    citeulike-linkout-0 = {http://dx.doi.org/10.1109/trustcom.2011.12},
    citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=6120804},
    doi = {10.1109/trustcom.2011.12},
    institution = {Dept. Of Electr. Eng. ETSI Telecomun., Univ. Politec. de Madrid, Madrid, Spain},
    isbn = {978-1-4577-2135-9},
    keywords = {dfraga, greenlsi, josem, repsys, security},
    month = nov,
    pages = {61--72},
    posted-at = {2016-02-27 11:36:31},
    priority = {2},
    publisher = {IEEE},
    title = {Holistic Solution for Confining Insider Attacks in Wireless Sensor Networks Using Reputation Systems Coupled with Clustering Techniques},
    url = {http://dx.doi.org/10.1109/trustcom.2011.12},
    year = {2011}
    }

  • D. Fraga, Á. Gutiérrez, J. C. Vallejo, A. Campo, and Z. Bankovic, “Improving social odometry robot networks with distributed reputation systems for collaborative purposes,” Sensors, vol. 11, iss. 12, pp. 11372-11389, 2011. doi:10.3390/s111211372
    [BibTeX] [Abstract] [Download PDF]

    The improvement of odometry systems in collaborative robotics remains an important challenge for several applications. Social odometry is a social technique which confers the robots the possibility to learn from the others. This paper analyzes social odometry and proposes and follows a methodology to improve its behavior based on cooperative reputation systems. We also provide a reference implementation that allows us to compare the performance of the proposed solution in highly dynamic environments with the performance of standard social odometry techniques. Simulation results quantitatively show the benefits of this collaborative approach that allows us to achieve better performances than social odometry.

    @article{FGV+11,
    abstract = {The improvement of odometry systems in collaborative robotics remains an important challenge for several applications. Social odometry is a social technique which confers the robots the possibility to learn from the others. This paper analyzes social odometry and proposes and follows a methodology to improve its behavior based on cooperative reputation systems. We also provide a reference implementation that allows us to compare the performance of the proposed solution in highly dynamic environments with the performance of standard social odometry techniques. Simulation results quantitatively show the benefits of this collaborative approach that allows us to achieve better performances than social odometry.},
    author = {Fraga, David and Guti\'{e}rrez, \'{A}lvaro and Vallejo, Juan C. and Campo, Alexandre and Bankovic, Zorana},
    citeulike-article-id = {10087787},
    citeulike-linkout-0 = {http://dx.doi.org/10.3390/s111211372},
    day = {30},
    doi = {10.3390/s111211372},
    issn = {1424-8220},
    journal = {Sensors},
    keywords = {dfraga, greenlsi, repsys, security},
    month = nov,
    number = {12},
    pages = {11372--11389},
    posted-at = {2016-02-27 11:24:33},
    priority = {2},
    title = {Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes},
    url = {http://dx.doi.org/10.3390/s111211372},
    volume = {11},
    year = {2011}
    }

  • M. Zapater, P. Arroba, J. M. Moya, and Z. Banković, “A State-of-the-Art on energy efficiency in today’s datacentres: researcher’s contributions and practical approaches,” Upgrade, vol. 12, iss. 4, pp. 67-74, 2011.
    [BibTeX] [Abstract] [Download PDF]

    Energy efficiency has become an issue of great importance in today’s datacentres. Metrics like Top500, which measure speed and performance, are beginning to lose importance in favor of others such as Green500. In order to increase energy efficiency of datacentres and save energy costs, the research community proposes solutions from both the computing and the cooling point of view, while European and {US} Institutions publish best practice manuals on energy-efficiency for datacentre owners. However, even though best practices are beginning to be implemented, most of the solutions offered by researchers are not yet used in real production environments. This paper makes a survey of the solutions proposed by researchers as well as the practices that real datacentres apply in order to increase the energy-efficiency of their facilities, and to find the reasons that create this gap between research and innovation in datacentres.

    @article{ZAM+2011,
    abstract = {Energy efficiency has become an issue of great importance in today's datacentres. Metrics like Top500, which measure speed and performance, are beginning to lose importance in favor of others such as Green500. In order to increase energy efficiency of datacentres and save energy costs, the research community proposes solutions from both the computing and the cooling point of view, while European and {US} Institutions publish best practice manuals on energy-efficiency for datacentre owners. However, even though best practices are beginning to be implemented, most of the solutions offered by researchers are not yet used in real production environments. This paper makes a survey of the solutions proposed by researchers as well as the practices that real datacentres apply in order to increase the energy-efficiency of their facilities, and to find the reasons that create this gap between research and innovation in datacentres.},
    author = {Zapater, Marina and Arroba, Patricia and Moya, Jos\'{e} M. and Bankovi\'{c}, Zorana},
    citeulike-article-id = {11737365},
    citeulike-linkout-0 = {http://www.cepis.org/upgrade/media/zapater\_2011\_41.pdf},
    issn = {1684-5285},
    journal = {UPGRADE},
    keywords = {datacenters, dc, efficiency, energy, greenlsi, josem, state-of-art},
    number = {4},
    pages = {67--74},
    posted-at = {2014-03-01 08:51:04},
    priority = {2},
    publisher = {CEPIS},
    title = {A {State-of-the-Art} on Energy Efficiency in Today's Datacentres: Researcher's Contributions and Practical Approaches},
    url = {http://www.cepis.org/upgrade/media/zapater\_2011\_41.pdf},
    volume = {12},
    year = {2011}
    }

  • M. Zapater, J. de Goyeneche, J. M. Moya, P. Malagón, and Z. Bankovic, “Thermal-Aware optimization of heterogeneous systems,” in 26th Conference on Design of Circuits and Integrated Systems, 2011.
    [BibTeX]
    @inproceedings{ZGM+11,
    author = {Zapater, Marina and de Goyeneche, Juan-Mariano and Moya, Jos\'{e} M. and Malag\'{o}n, Pedro and Bankovic, Zorana},
    booktitle = {{26th Conference on Design of Circuits and Integrated Systems}},
    citeulike-article-id = {11737371},
    keywords = {datacenters, greenlsi, heterogeneous, josem},
    month = nov,
    posted-at = {2012-11-22 15:29:29},
    priority = {2},
    title = {{Thermal-Aware} Optimization of Heterogeneous Systems},
    year = {2011}
    }

  • Z. Bankovic, D. Fraga, J. M. Moya, J. C. Vallejo, P. Malagón, Á. Araujo, J. de Goyeneche, E. Romero, J. Blesa, D. Villanueva, and O. Nieto-Taladriz, “Improving security in WMNs with reputation systems and self-organizing maps,” Journal of network and computer applications, vol. 34, 2011. doi:10.1016/j.jnca.2010.03.023
    [BibTeX] [Abstract] [Download PDF]

    One of the most important problems of {WMNs}, that is even preventing them from being used in many sensitive applications, is the lack of security. To ensure security of {WMNs}, two strategies need to be adopted: embedding security mechanisms into the network protocols, and developing efficient intrusion detection and reaction systems. To date, many secure protocols have been proposed, but their role of defending attacks is very limited. We present a framework for intrusion detection in {WMNs} that is orthogonal to the network protocols. It is based on a reputation system, that allows to isolate ill-behaved nodes by rating their reputation as low, and distributed agents based on unsupervised learning algorithms (self-organizing maps), that are able to detect deviations from the normal behavior. An additional advantage of this approach is that it is quite independent of the attacks, and therefore it can detect and confine new, previously unknown, attacks. Unlike previous approaches, and due to the inherent insecurity of {WMN} nodes, we assume that confidentiality and integrity cannot be preserved for any single node.

    @article{BFM+11,
    abstract = {One of the most important problems of {WMNs}, that is even preventing them from being used in many sensitive applications, is the lack of security. To ensure security of {WMNs}, two strategies need to be adopted: embedding security mechanisms into the network protocols, and developing efficient intrusion detection and reaction systems. To date, many secure protocols have been proposed, but their role of defending attacks is very limited.
    We present a framework for intrusion detection in {WMNs} that is orthogonal to the network protocols. It is based on a reputation system, that allows to isolate ill-behaved nodes by rating their reputation as low, and distributed agents based on unsupervised learning algorithms (self-organizing maps), that are able to detect deviations from the normal behavior. An additional advantage of this approach is that it is quite independent of the attacks, and therefore it can detect and confine new, previously unknown, attacks. Unlike previous approaches, and due to the inherent insecurity of {WMN} nodes, we assume that confidentiality and integrity cannot be preserved for any single node.},
    author = {Bankovic, Zorana and Fraga, David and Moya, Jos\'{e} M. and Vallejo, Juan C. and Malag\'{o}n, Pedro and Araujo, \'{A}lvaro and de Goyeneche, Juan-Mariano and Romero, Elena and Blesa, Javier and Villanueva, Daniel and Nieto-Taladriz, Octavio},
    citeulike-article-id = {11722637},
    citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.jnca.2010.03.023},
    doi = {10.1016/j.jnca.2010.03.023},
    journal = {Journal of Network and Computer Applications},
    keywords = {greenlsi, josem, ms},
    posted-at = {2012-11-18 22:02:50},
    priority = {2},
    title = {{Improving security in WMNs with reputation systems and self-organizing maps}},
    url = {http://dx.doi.org/10.1016/j.jnca.2010.03.023},
    volume = {34},
    year = {2011}
    }

2010

  • Z. Bankovic, J. C. Vallejo, P. Malagón, Á. Araujo, and J. M. Moya, “Eliminating routing protocol anomalies in wireless sensor networks using AI techniques,” in Aisec, 2010, pp. 8-13. doi:10.1145/1866423.1866426
    [BibTeX] [Abstract] [Download PDF]

    The specific nature of routing in sensor networks has made possible new sorts of attacks that can have closer insight and effect on the networks packets, the most important being the packet tampering. Routing attacks on the network level are the first step in tampering with the packets. In this work we propose a solution for detecting and eliminating these attacks that couples reputation systems with clustering techniques, namely unsupervised genetic algorithm and self-organizing maps, trained for detecting outliers in data. The algorithms use the feature space based on sequences of routing hops that provides ability of detecting wide range of attacks. We further present a flexible way of integrating the solution into targeted sensor network that can easily adapt its computational requirements to the existing network resources. The solution offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability, flexible integration, and high ability in detecting and confining attacks.

    @inproceedings{BVM+10,
    abstract = {The specific nature of routing in sensor networks has made possible new sorts of attacks that can have closer insight and effect on the networks packets, the most important being the packet tampering. Routing attacks on the network level are the first step in tampering with the packets. In this work we propose a solution for detecting and eliminating these attacks that couples reputation systems with clustering techniques, namely unsupervised genetic algorithm and self-organizing maps, trained for detecting outliers in data. The algorithms use the feature space based on sequences of routing hops that provides ability of detecting wide range of attacks. We further present a flexible way of integrating the solution into targeted sensor network that can easily adapt its computational requirements to the existing network resources. The solution offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability, flexible integration, and high ability in detecting and confining attacks.},
    author = {Bankovic, Zorana and Vallejo, Juan C. and Malag\'{o}n, Pedro and Araujo, \'{A}lvaro and Moya, Jos\'{e} M.},
    booktitle = {AISec},
    citeulike-article-id = {11733818},
    citeulike-linkout-0 = {http://dx.doi.org/10.1145/1866423.1866426},
    citeulike-linkout-1 = {http://dl.acm.org/citation.cfm?doid=1866423.1866426},
    doi = {10.1145/1866423.1866426},
    editor = {Greenstadt, Rachel},
    isbn = {978-1-4503-0088-9},
    keywords = {greenlsi, josem},
    pages = {8--13},
    posted-at = {2015-11-13 23:48:18},
    priority = {2},
    publisher = {ACM},
    title = {Eliminating routing protocol anomalies in wireless sensor networks using {AI} techniques},
    url = {http://dl.acm.org/citation.cfm?doid=1866423.1866426},
    year = {2010}
    }

  • Z. Banković, J. M. Moya, Á. Araujo, D. Fraga, J. C. Vallejo, and J. de Goyeneche, “Distributed intrusion detection system for wireless sensor networks based on a reputation system coupled with kernel self-organizing maps,” Integrated computer-aided engineering, vol. 17, iss. 2, pp. 87-102, 2010. doi:10.3233/ICA-2010-0334
    [BibTeX] [Abstract] [Download PDF]

    Security of sensor networks is a complicated task, mostly due to the limited resources of sensor units. The first line of defense, i.e. encryption and authentication, is useless if an attacker has entered the system, and it is also vulnerable to side-channel attacks. Thus, a second line of defense, known as Intrusion Detection, must be added in order to detect and eliminate attacks. In the recent past, various solutions for detecting intrusions have been proposed. Most of them are able to detect only a limited number of attacks. Further, the solutions that deploy machine learning techniques exhibit higher level of flexibility and adaptability. Yet, these techniques consume significant power and computational resources. In this work we propose a distributed intrusion detection system organized as a reputation system where the reputation of each node is assigned by self-organizing maps ({SOM}) trained for detecting intrusions. The response of the system consists in assigning low reputation values to the compromised nodes rendering them isolated from the rest of the network. Further, we propose the implementation of {SOM} algorithm using the energy-efficient {SORU} (Stream Oriented Reconfigurable Unit) co-processor developed by our research group. Our solution offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability and energy efficiency. The testing results demonstrate its high potential.

    @article{BMA+13,
    abstract = {Security of sensor networks is a complicated task, mostly due to the limited resources of sensor units. The first line of defense, i.e. encryption and authentication, is useless if an attacker has entered the system, and it is also vulnerable to side-channel attacks. Thus, a second line of defense, known as Intrusion Detection, must be added in order to detect and eliminate attacks. In the recent past, various solutions for detecting intrusions have been proposed. Most of them are able to detect only a limited number of attacks. Further, the solutions that deploy machine learning techniques exhibit higher level of flexibility and adaptability. Yet, these techniques consume significant power and computational resources. In this work we propose a distributed intrusion detection system organized as a reputation system where the reputation of each node is assigned by self-organizing maps ({SOM}) trained for detecting intrusions. The response of the system consists in assigning low reputation values to the compromised nodes rendering them isolated from the rest of the network. Further, we propose the implementation of {SOM} algorithm using the energy-efficient {SORU} (Stream Oriented Reconfigurable Unit) co-processor developed by our research group. Our solution offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability and energy efficiency. The testing results demonstrate its high potential.},
    author = {Bankovi\'{c}, Zorana and Moya, Jos\'{e} M. and Araujo, \'{A}lvaro and Fraga, David and Vallejo, Juan C. and de Goyeneche, Juan-Mariano},
    citeulike-article-id = {12289896},
    citeulike-linkout-0 = {http://dx.doi.org/10.3233/ICA-2010-0334},
    day = {1},
    doi = {10.3233/ICA-2010-0334},
    journal = {Integrated Computer-Aided Engineering},
    keywords = {ami, greenlsi, josem, security},
    month = jan,
    number = {2},
    pages = {87--102},
    posted-at = {2013-04-22 08:40:29},
    priority = {2},
    title = {Distributed intrusion detection system for wireless sensor networks based on a reputation system coupled with kernel self-organizing maps},
    url = {http://dx.doi.org/10.3233/ICA-2010-0334},
    volume = {17},
    year = {2010}
    }

  • M. Zapater, J. L. Risco, J. L. Ayala, and J. M. Moya, “Combined Dynamic-Static approach for Thermal-Awareness in heterogeneous data centers,” in Iwia, , 2010. doi:10.1109/IWIA.2010.7
    [BibTeX] [Abstract] [Download PDF]

    The thermal profile of data centers plays a significant role in affecting the cooling cost and power budget of the system. While several dynamic and static approaches have been proposed so far, these have failed on considering the whole picture. This paper proposes a combined static and dynamic approach that shows the benefits of the efficient scheduling strategies on leading to thermal-efficient floorplans. The devised methodology comes out with a placement of processors and task scheduling for a heterogeneous system, where the main thermal metrics (maximum temperature and thermal gradient) have been optimized.

    @inbook{ZRAM12,
    abstract = {The thermal profile of data centers plays a significant role in affecting the cooling cost and power budget of the system. While several dynamic and static approaches have been proposed so far, these have failed on considering the whole picture. This paper proposes a combined static and dynamic approach that shows the benefits of the efficient scheduling strategies on leading to thermal-efficient floorplans. The devised methodology comes out with a placement of processors and task scheduling for a heterogeneous system, where the main thermal metrics (maximum temperature and thermal gradient) have been optimized.},
    author = {Zapater, Marina and Risco, Jose L. and Ayala, Jose L. and Moya, Jose M.},
    booktitle = {IWIA},
    citeulike-article-id = {11737377},
    citeulike-linkout-0 = {http://dx.doi.org/10.1109/IWIA.2010.7},
    doi = {10.1109/IWIA.2010.7},
    keywords = {datacenters, greenlsi, josem},
    posted-at = {2012-11-22 15:27:59},
    priority = {2},
    title = {Combined {Dynamic-Static} Approach for {Thermal-Awareness} in Heterogeneous Data Centers},
    url = {http://dx.doi.org/10.1109/IWIA.2010.7},
    year = {2010}
    }

  • Z. Banković, D. Fraga, J. M. Moya, J. C. Vallejo, Á. Araujo, P. Malagón, J. Goyeneche, D. Villanueva, E. Romero, and J. Blesa, “Detecting and confining sybil attack in wireless sensor networks based on reputation systems coupled with self-organizing maps,” in Artificial intelligence applications and innovations, H. Papadopoulos, A. Andreou, and M. Bramer, Eds., Springer Berlin Heidelberg, 2010, vol. 339, pp. 311-318. doi:10.1007/978-3-642-16239-8_41
    [BibTeX] [Abstract] [Download PDF]

    The Sybil attack is one of the most aggressive and evasive attacks in sensor networks that can affect on many aspects of network functioning. Thus, its efficient detection is of highest importance. In order to resolve this issue, in this work we propose to couple reputation systems with agents based on self-organizing map algorithm trained for detecting outliers in data. The response of the system consists in assigning low reputation values to the compromised node rendering them isolated from the rest of the network. The main improvement of this work consists in the way of calculating reputation, which is more flexible and discriminative in distinguishing attacks from normal behavior. Self-organizing map algorithm deploys feature space based on sequences of sensor outputs. Our solution offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability and low consumption. The testing results demonstrate its high ability in detecting and confining Sybil attack.

    @incollection{BFM+10,
    abstract = {The Sybil attack is one of the most aggressive and evasive attacks in sensor networks that can affect on many aspects of network functioning. Thus, its efficient detection is of highest importance. In order to resolve this issue, in this work we propose to couple reputation systems with agents based on self-organizing map algorithm trained for detecting outliers in data. The response of the system consists in assigning low reputation values to the compromised node rendering them isolated from the rest of the network. The main improvement of this work consists in the way of calculating reputation, which is more flexible and discriminative in distinguishing attacks from normal behavior. Self-organizing map algorithm deploys feature space based on sequences of sensor outputs. Our solution offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability and low consumption. The testing results demonstrate its high ability in detecting and confining Sybil attack.},
    author = {Bankovi\'{c}, Zorana and Fraga, David and Moya, Jos\'{e} M. and Vallejo, Juan C. and Araujo, \'{A}lvaro and Malag\'{o}n, Pedro and Goyeneche, Juan-Mariano and Villanueva, Daniel and Romero, Elena and Blesa, Javier},
    booktitle = {Artificial Intelligence Applications and Innovations},
    citeulike-article-id = {11723369},
    citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-16239-8\_41},
    doi = {10.1007/978-3-642-16239-8\_41},
    editor = {Papadopoulos, Harris and Andreou, AndreasS and Bramer, Max},
    keywords = {detection, greenlsi, josem, maps, networks, outlier, reputation, self-organizing, sensor, system, wireless},
    pages = {311--318},
    posted-at = {2012-11-18 23:56:53},
    priority = {0},
    publisher = {Springer Berlin Heidelberg},
    series = {IFIP Advances in Information and Communication Technology},
    title = {Detecting and Confining Sybil Attack in Wireless Sensor Networks Based on Reputation Systems Coupled with Self-organizing Maps},
    url = {http://dx.doi.org/10.1007/978-3-642-16239-8\_41},
    volume = {339},
    year = {2010}
    }

  • Z. Banković, J. C. Vallejo, P. Malagón, Á. Araujo, and J. M. Moya, “Eliminating routing protocol anomalies in wireless sensor networks using AI techniques,” in ACM Conference on Computer and Communications Security, 2010. doi:10.1145/1866423.1866426
    [BibTeX] [Abstract] [Download PDF]

    The specific nature of routing in sensor networks has made possible new sorts of attacks that can have closer insight and effect on the networks packets, the most important being the packet tampering. Routing attacks on the network level are the first step in tampering with the packets. In this work we propose a solution for detecting and eliminating these attacks that couples reputation systems with clustering techniques, namely unsupervised genetic algorithm and self-organizing maps, trained for detecting outliers in data. The algorithms use the feature space based on sequences of routing hops that provides ability of detecting wide range of attacks. We further present a flexible way of integrating the solution into targeted sensor network that can easily adapt its computational requirements to the existing network resources. The solution offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability, flexible integration, and high ability in detecting and confining attacks.

    @inproceedings{BVM+10,
    abstract = {The specific nature of routing in sensor networks has made possible new sorts of attacks that can have closer insight and effect on the networks packets, the most important being the packet tampering. Routing attacks on the network level are the first step in tampering with the packets. In this work we propose a solution for detecting and eliminating these attacks that couples reputation systems with clustering techniques, namely unsupervised genetic algorithm and self-organizing maps, trained for detecting outliers in data. The algorithms use the feature space based on sequences of routing hops that provides ability of detecting wide range of attacks. We further present a flexible way of integrating the solution into targeted sensor network that can easily adapt its computational requirements to the existing network resources. The solution offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability, flexible integration, and high ability in detecting and confining attacks.},
    author = {Bankovi\'{c}, Zorana and Vallejo, Juan C. and Malag\'{o}n, Pedro and Araujo, \'{A}lvaro and Moya, Jos\'{e} M.},
    booktitle = {{ACM Conference on Computer and Communications Security}},
    citeulike-article-id = {11722639},
    citeulike-linkout-0 = {http://dx.doi.org/10.1145/1866423.1866426},
    doi = {10.1145/1866423.1866426},
    keywords = {greenlsi, josem, ms},
    posted-at = {2012-11-18 22:02:52},
    priority = {2},
    title = {{Eliminating routing protocol anomalies in wireless sensor networks using AI techniques}},
    url = {http://dx.doi.org/10.1145/1866423.1866426},
    year = {2010}
    }

2009

  • I. Recio, J. M. Moya, Á. Araujo, J. C. Vallejo, and P. Malagón, “Analysis and design of an object tracking service for intelligent environments,” in Iwann (2), 2009, pp. 914-921. doi:10.1007/978-3-642-02481-8_139
    [BibTeX] [Abstract] [Download PDF]

    This paper describes the design and implementation of an object tracking service for indoor environments. First, the wireless indoor location estimation technology is overviewed presenting advantages and disadvantages. Second, the methodology of the study is presented. To estimate the position we use clues inserted by location clue injectors of the system. In our architecture one of these injectors is a {ZigBee} sensor network. As location algorithm we have developed a method combining statistical techniques (particle filter) and proximity sensing (nearest neighbour) to get better efficiency. The results obtained show that a good precision and reliability can be achieved with a low-cost solution.

    @inproceedings{RMA+09,
    abstract = {This paper describes the design and implementation of an object tracking service for indoor environments. First, the wireless indoor location estimation technology is overviewed presenting advantages and disadvantages. Second, the methodology of the study is presented. To estimate the position we use clues inserted by location clue injectors of the system. In our architecture one of these injectors is a {ZigBee} sensor network. As location algorithm we have developed a method combining statistical techniques (particle filter) and proximity sensing (nearest neighbour) to get better efficiency. The results obtained show that a good precision and reliability can be achieved with a low-cost solution.},
    author = {Recio, Ignacio and Moya, Jos\'{e} M. and Araujo, \'{A}lvaro and Vallejo, Juan C. and Malag\'{o}n, Pedro},
    booktitle = {IWANN (2)},
    citeulike-article-id = {11733951},
    citeulike-linkout-0 = {http://link.springer.com/chapter/10.1007\%2F978-3-642-02481-8\_139},
    citeulike-linkout-1 = {http://dx.doi.org/10.1007/978-3-642-02481-8\_139},
    doi = {10.1007/978-3-642-02481-8\_139},
    editor = {Omatu, Sigeru and Rocha, Miguel and Bravo, Jos\'{e} and Riverola, Florentino F. and Corchado, Emilio and Bustillo, Andr\'{e}s and Corchado, Juan M.},
    keywords = {greenlsi, josem},
    pages = {914--921},
    posted-at = {2015-11-13 23:47:01},
    priority = {2},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    title = {Analysis and Design of an Object Tracking Service for Intelligent Environments},
    url = {http://link.springer.com/chapter/10.1007\%2F978-3-642-02481-8\_139},
    volume = {5518},
    year = {2009}
    }

  • J. M. Moya, J. C. Vallejo, P. Malagón, Á. Araujo, J. de Goyeneche, and O. Nieto-Taladriz, “A scalable security framework for reliable AmI applications based on untrusted sensors,” in Wwic, 2009, pp. 73-84. doi:10.1007/978-3-642-02118-3_7
    [BibTeX] [Abstract] [Download PDF]

    Security in Ambient Intelligence ({AmI}) poses too many challenges due to the inherently insecure nature of wireless sensor nodes. However, there are two characteristics of these environments that can be used effectively to prevent, detect, and confine attacks: redundancy and continuous adaptation. In this article we propose a global strategy and a system architecture to cope with security issues in {AmI} applications at different levels. Unlike in previous approaches, we assume an individual wireless node is vulnerable. We present an agent-based architecture with supporting services that is proven to be adequate to detect and confine common attacks. Decisions at different levels are supported by a trust-based framework with good and bad reputation feedback while maintaining resistance to bad-mouthing attacks. We also propose a set of services that can be used to handle identification, authentication, and authorization in intelligent ambients. The resulting approach takes into account practical issues, such as resource limitation, bandwidth optimization, and scalability.

    @inproceedings{MVM+09,
    abstract = {Security in Ambient Intelligence ({AmI}) poses too many challenges due to the inherently insecure nature of wireless sensor nodes. However, there are two characteristics of these environments that can be used effectively to prevent, detect, and confine attacks: redundancy and continuous adaptation. In this article we propose a global strategy and a system architecture to cope with security issues in {AmI} applications at different levels. Unlike in previous approaches, we assume an individual wireless node is vulnerable.
    We present an agent-based architecture with supporting services that is proven to be adequate to detect and confine common attacks. Decisions at different levels are supported by a trust-based framework with good and bad reputation feedback while maintaining resistance to bad-mouthing attacks. We also propose a set of services that can be used to handle identification, authentication, and authorization in intelligent ambients.
    The resulting approach takes into account practical issues, such as resource limitation, bandwidth optimization, and scalability.},
    author = {Moya, Jos\'{e} M. and Vallejo, Juan C. and Malag\'{o}n, Pedro and Araujo, \'{A}lvaro and de Goyeneche, Juan-Mariano and Nieto-Taladriz, Octavio},
    booktitle = {WWIC},
    citeulike-article-id = {11733964},
    citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-02118-3\_7},
    citeulike-linkout-1 = {http://link.springer.com/chapter/10.1007\%2F978-3-642-02118-3\_7},
    doi = {10.1007/978-3-642-02118-3\_7},
    editor = {van den Berg, Hans and Heijenk, Geert J. and Osipov, Evgeny and Staehle, Dirk},
    keywords = {greenlsi, josem, security},
    pages = {73--84},
    posted-at = {2012-11-22 15:35:11},
    priority = {2},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    title = {A Scalable Security Framework for Reliable {AmI} Applications Based on Untrusted Sensors},
    url = {http://link.springer.com/chapter/10.1007\%2F978-3-642-02118-3\_7},
    volume = {5546},
    year = {2009}
    }

  • J. M. Moya, J. C. Vallejo, D. Fraga, Á. Araujo, D. Villanueva, and J. de Goyeneche, “Using Reputation Systems and Non-Deterministic Routing to Secure Wireless Sensor Networks,” Sensors, vol. 9, 2009. doi:10.3390/s90503958
    [BibTeX] [Abstract] [Download PDF]

    Security in wireless sensor networks is difficult to achieve because of the resource limitations of the sensor nodes. We propose a trust-based decision framework for wireless sensor networks coupled with a non-deterministic routing protocol. Both provide a mechanism to effectively detect and confine common attacks, and, unlike previous approaches, allow bad reputation feedback to the network. This approach has been extensively simulated, obtaining good results, even for unrealistically complex attack scenarios.

    @article{MVF+09,
    abstract = {Security in wireless sensor networks is difficult to achieve because of the resource limitations of the sensor nodes. We propose a trust-based decision framework for wireless sensor networks coupled with a non-deterministic routing protocol. Both provide a mechanism to effectively detect and confine common attacks, and, unlike previous approaches, allow bad reputation feedback to the network. This approach has been extensively simulated, obtaining good results, even for unrealistically complex attack scenarios.},
    author = {Moya, Jos\'{e} M. and Vallejo, Juan C. and Fraga, David and Araujo, \'{A}lvaro and Villanueva, Daniel and de Goyeneche, Juan-Mariano},
    citeulike-article-id = {11722641},
    citeulike-linkout-0 = {http://dx.doi.org/10.3390/s90503958},
    doi = {10.3390/s90503958},
    journal = {Sensors},
    keywords = {greenlsi, josem, ms},
    posted-at = {2012-11-18 22:02:53},
    priority = {2},
    title = {{Using Reputation Systems and Non-Deterministic Routing to Secure Wireless Sensor Networks}},
    url = {http://dx.doi.org/10.3390/s90503958},
    volume = {9},
    year = {2009}
    }

  • J. M. Moya, Á. Araujo, Z. Banković, J. de Goyeneche, J. C. Vallejo, P. Malagón, D. Villanueva, D. Fraga, E. Romero, and J. Blesa, “Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps,” Sensors, vol. 9, 2009. doi:10.3390/s91109380
    [BibTeX] [Abstract] [Download PDF]

    The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition ({SCADA}) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.

    @article{MAB+09,
    abstract = {The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition ({SCADA}) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.},
    author = {Moya, Jos\'{e} M. and Araujo, \'{A}lvaro and Bankovi\'{c}, Zorana and de Goyeneche, Juan-Mariano and Vallejo, Juan C. and Malag\'{o}n, Pedro and Villanueva, Daniel and Fraga, David and Romero, Elena and Blesa, Javier},
    citeulike-article-id = {11722640},
    citeulike-linkout-0 = {http://dx.doi.org/10.3390/s91109380},
    doi = {10.3390/s91109380},
    journal = {Sensors},
    keywords = {greenlsi, josem, ms},
    posted-at = {2012-11-18 22:02:53},
    priority = {2},
    title = {{Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps}},
    url = {http://dx.doi.org/10.3390/s91109380},
    volume = {9},
    year = {2009}
    }