Patricia Arroba

Research Assistant
+34 91 549 5700, ext 4216
Room B-039.H
Contact Patricia Arroba

I am Ph.D. Student in the Electronic Engineering Department at the Escuela Técnica Superior de Ingenieros de Telecomunicación of the Universidad Politécnica de Madrid since October 2012 under the guidance of Prof. José M. Moya and Prof. José L. Ayala. Prof. Rajkumar Buyya from The University of Melbourne is also my external mentor.

My research focuses on energy optimization and modeling of complex systems and the development of energy efficient techniques for cloud computing data centers.

I received my B.Sc and M.Sc. degree in 2011 in Telecommunication Engineering and a M.Sc. in Electronic Systems Engineering in 2012 from the Technical University of Madrid (Universidad Politécnica de Madrid), Spain.

I joined CLOUDS Lab. in October 2014 as a visitor student at the University of Melbourne under the supervision of Prof. Rajkumar Buyya. My research stay was sponsored by the European Network of Excellence on High Performance and Embedded Architecture and Compilation (HiPEAC).

Currently, I am a Visiting Ph.D. Student at the Tokyo Institute of Technology under the supervision of Prof. Satoshi Matsuoka. This research stay is sponsored by the European Commission under the Erasmus Mundus Euro-Asian Sustainable Energy Development programme.

My current research focuses on the design and development of thermal and power-aware algorithms to optimize the energy consumption of Cloud infrastructures.

Publications

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, p. 1249–1256, 2017. doi:10.1109/tii.2017.2693365
    [BibTeX] [Download PDF]
    @article{citeulike:14427500,
    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 = {greenlsi},
    month = jun,
    number = {3},
    pages = {1249--1256},
    posted-at = {2017-09-08 15:13:49},
    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}
    }

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{Zapater:ASOC:2016,
    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},
    month = aug,
    posted-at = {2016-08-22 15:14:02},
    priority = {0},
    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]

    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{citeulike:13989456,
    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},
    keywords = {cloud, greendisc, greenlsi},
    posted-at = {2016-03-28 12:26:20},
    priority = {5},
    title = {Proactive Power and Thermal Aware Optimizations for {Energy-Efficient} Cloud Computing},
    year = {2016}
    }

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{citeulike:13989454,
    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 12:14:27},
    priority = {2},
    title = {{DVFS}-­Aware Consolidation for {Energy-Efficient} Clouds},
    url = {http://dx.doi.org/10.1109/PACT.2015.59},
    year = {2015}
    }

  • M. Zapater, P. Arroba, J. Rodrigo, K. Herrero, and J. Fernandez, “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{citeulike:13696422,
    author = {Zapater, Marina and Arroba, Patricia and Rodrigo, Jos\'{e}LuisAyala and Herrero, KatzalinOlcoz and Fernandez, Jos\'{e}ManuelMoya},
    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},
    month = jan,
    pages = {267-286+},
    posted-at = {2016-03-28 11:55:34},
    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.
    [BibTeX] [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.

    @inproceedings{iaransay:IEEECloud:2015,
    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},
    keywords = {cloud, ee, greenlsi},
    posted-at = {2016-03-28 11:54:27},
    priority = {2},
    publisher = {IEEE},
    title = {A Trust and Reputation system for energy optimization in Cloud data centers},
    year = {2015}
    }

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.
    [BibTeX]
    @inproceedings{citeulike:13166637,
    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},
    keywords = {ee, greendisc, greenlsi},
    posted-at = {2016-03-28 11:55:44},
    priority = {2},
    title = {Server Power Modeling for {Run-Time} Energy Optimization of Cloud Computing Facilities},
    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{Arroba:MMEHB:2014,
    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},
    posted-at = {2016-03-28 11:55:41},
    priority = {2},
    title = {Evolutionary Power Modeling for High-end Servers in Cloud Data Centers},
    year = {2014}
    }

  • P. Arroba, J. Risco-Martín, M. Zapater, J. Moya, and J. Ayala, “Enhancing regression models for complex systems using evolutionary techniques for feature engineering,” , p. 1–15, 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{Arroba:JGRID:2014,
    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},
    booktitle = {Journal of Grid Computing},
    citeulike-article-id = {13696419},
    citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10723-014-9313-8},
    citeulike-linkout-1 = {http://link.springer.com/article/10.1007/s10723-014-9313-8},
    doi = {10.1007/s10723-014-9313-8},
    keywords = {cloud, ee, greenlsi},
    pages = {1--15},
    posted-at = {2016-03-28 11:55:38},
    priority = {2},
    publisher = {Springer Netherlands},
    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}
    }

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]

    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{citeulike:12651866,
    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},
    keywords = {greendisc, greenlsi, modeling},
    month = sep,
    posted-at = {2016-03-28 11:56:07},
    priority = {2},
    title = {On the {Leakage-Power} Modeling for Optimal Server Operation},
    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{citeulike:12651885,
    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, greenlsi, wsn},
    month = nov,
    posted-at = {2016-03-28 11:56:03},
    priority = {2},
    title = {A {Cyber-Physical} Approach to Combined {HW}-{SW} Monitoring for Improving Energy Efficiency in Data Centers},
    year = {2013}
    }

  • 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, 2013. 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+14,
    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 = dec,
    number = {0},
    posted-at = {2016-03-28 11:55:57},
    priority = {2},
    title = {A novel energy-driven computing paradigm for e-health scenarios},
    url = {http://www.sciencedirect.com/science/article/pii/S0167739X13002768},
    year = {2013}
    }

2011

  • P. Arroba, J. C. Vallejo, Á. Araujo, D. Fraga, and J. M. Moya, “A methodology for developing accessible mobile platforms over leading devices for visually impaired people,” , J. Bravo, R. Hervás, and V. Villarreal, Eds., Springer Berlin Heidelberg, 2011, vol. 6693, p. 209–215. doi:10.1007/978-3-642-21303-8_29
    [BibTeX] [Abstract] [Download PDF]

    Mobile user interfaces are moving to new touchscreen technologies setting new barriers for the blind. Many solutions and designs have been proposed but none is complete for the vast heterogeneous variety of devices. In this paper, we present a methodology for developing an accessible-to-blind platform based on the principles that visually impaired people should be able to access leading technology and no specific hardware should be necessary for it. Besides, our solution provides input and output methods adapted to any underlying hardware as proof of concept and a guidelines for developing mobile platforms and applications.

    @inbook{citeulike:13991534,
    abstract = {Mobile user interfaces are moving to new touchscreen technologies setting new barriers for the blind. Many solutions and designs have been proposed but none is complete for the vast heterogeneous variety of devices.
    In this paper, we present a methodology for developing an accessible-to-blind platform based on the principles that visually impaired people should be able to access leading technology and no specific hardware should be necessary for it. Besides, our solution provides input and output methods adapted to any underlying hardware as proof of concept and a guidelines for developing mobile platforms and applications.},
    author = {Arroba, Patricia and Vallejo, Juan C. and Araujo, \'{A}lvaro and Fraga, David and Moya, Jos\'{e} M.},
    citeulike-article-id = {13991534},
    citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-21303-8\_29},
    doi = {10.1007/978-3-642-21303-8\_29},
    editor = {Bravo, Jos\'{e} and Herv\'{a}s, Ram\'{o}n and Villarreal, Vladimir},
    isbn = {978-3-642-21302-1},
    journal = {Ambient Assisted Living},
    keywords = {greenlsi, methodology},
    pages = {209--215},
    posted-at = {2016-03-31 11:27:05},
    priority = {2},
    publisher = {Springer Berlin Heidelberg},
    title = {A Methodology for Developing Accessible Mobile Platforms over Leading Devices for Visually Impaired People},
    url = {http://dx.doi.org/10.1007/978-3-642-21303-8\_29},
    volume = {6693},
    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, p. 67–74, 2011.
    [BibTeX] [Download PDF]
    @article{parroba_CEPIS_2011,
    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 = {dc, greenlsi, state-of-art},
    number = {4},
    pages = {67--74},
    posted-at = {2012-12-17 16:33:58},
    priority = {0},
    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}
    }