Leveraging heterogeneity for energy minimization in data centers

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.

A cyber-physical approach to combined HW-SW monitoring for improving energy efficiency in data centers

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 Net- work (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.

Leakage and temperature aware server control for improving energy efficiency in data centers

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 computa- tional 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.

Proactive and reactive thermal aware optimization techniques to minimize the environmental impact of data centers

Data centers are easily found in every sector of the worldwide economy. They are composed 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 grep 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.

Diseño de un sistema de suministro energético eficiente para centros de datos (DATA CENTERS) con tecnología solar fotovoltaica

El objetivo de este trabajo fin de grado es el de analizar las distintas posibilidades de suministro del consumo eléctrico de un centro de datos mediante la combinación de instalaciones solares fotovoltaicas. Estos centros son imprescindibles y de enorme importancia en la actualidad; la cantidad de energía eléctrica consumida por éstos en todo el mundo se ha duplicado, y esta tendencia ha ido creciendo en los últimos años, provocado principalmente por un uso cada vez más extendido socialmente de las nuevas tecnologías. Para que sean energéticamente eficientes toma un papel fundamental la tecnología fotovoltaica. Este proyecto se aplicará al Centro de Supercomputación y Visualización de Madrid (CeSViMa), centro de datos de la Universidad Politécnica de Madrid. Para un centro como éste además de los costes de energía para el mantenimiento también debemos añadir las infraestructuras de climatización con un alto consumo de electricidad. Aunque en los últimos años han centrado sus esfuerzos en la diversificación de servicios para optimizar recursos, tienen consumos muy altos. Si todo esto lo unimos a un emplazamiento idóneo para este tipo de tecnología, determina una gran oportunidad. El diseño propuesto en este trabajo fin de grado se adaptará a toda su infraestructura, aportando soluciones con la última tecnología, avalada mediante simulaciones y estudios que aseguraran una mejora significativa tanto energética como económica y que brindan para este centro de una gran oportunidad de mejora.

Proactive and reactive thermal aware optimization techniques to minimize the environmental impact of data centers

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.

Design and implementation of an adaptive dashboard and visualization interface for the dynamic optimization and control of Data Centers

Over the last few years, the Data Center market has increased exponentially and this tendency continues today. As a direct consequence of this trend, the industry is pushing the development and implementation of different new technologies that would improve the energy consumption efficiency of data centers. An adaptive dashboard would allow the user to monitor the most important parameters of a data center in real time. For that reason, monitoring companies work with IoT big data filtering tools and cloud computing systems to handle the amounts of data obtained from the sensors placed in a data center.Analyzing the market trends in this field we can affirm that the study of predictive algorithms has become an essential area for competitive IT companies. Complex algorithms are used to forecast risk situations based on historical data and warn the user in case of danger. Considering that several different users will interact with this dashboard from IT experts or maintenance staff to accounting managers, it is vital to personalize it automatically. Following that line of though, the dashboard should only show relevant metrics to the user in different formats like overlapped maps or representative graphs among others. These maps will show all the information needed in a visual and easy-to-evaluate way. To sum up, this dashboard will allow the user to visualize and control a wide range of variables. Monitoring essential factors such as average temperature, gradients or hotspots as well as energy and power consumption and savings by rack or building would allow the client to understand how his equipment is behaving, helping him to optimize the energy consumption and efficiency of the racks. It also would help him to prevent possible damages in the equipment with predictive high-tech algorithms.

An Internet of Things approach for managing smart services provided by wearable devices.

The Internet of Things (IoT) is growing at a fast pace with new devices getting connected all the time. A new emerging group of these devices are the wearable devices, and Wireless Sensor Networks are a good way to integrate them in the IoT concept and bring new experiences to the daily life activities. In this paper we present an everyday life application involving a WSN as the base of a novel context-awareness sports scenario where physiological parameters are measured and sent to the WSN by wearable devices. Applications with several hardware components introduce the problem of heterogeneity in the network. In order to integrate different hardware platforms and to introduce a service-oriented semantic middleware solution into a single application, we propose the use of an Enterprise Service Bus (ESB) as a bridge for guaranteeing interoperability and integration of the different environments, thus introducing a semantic added value needed in the world of IoT-based systems. This approach places all the data acquired (e.g., via Internet data access) at application developers disposal, opening the system to new user applications. The user can then access the data through a wide variety of devices (smartphones, tablets, computers) and Operating Systems (Android, iOS, Windows, Linux, etc.).

Diseño y configuración de una red de comunicaciones en un Centro de Contactos - Contact Center

Este proyecto muestra una solución de red para una empresa que presta servicios de Contact Center desde distintas sedes distribuidas geográficamente, utilizando la tecnología de telefonía sobre IP. El objetivo de este proyecto es el de convertirse en una guía de diseño para el despliegue de soluciones de red utilizando los actuales equipos de comunicaciones desarrollados por el fabricante Cisco Systems, Inc., los equipos de seguridad desarrollados por el fabricante Fortinet y los sistemas de telefonía desarrollados por Avaya Inc. y Oracle Corporation, debido a su gran penetración en el mercado y a las aportaciones que cada uno ha realizado en el sector de Contact Center. Para poder proveer interconexión entre las sedes de un Contact Center se procede a la contratación de un acceso a la red MPLS perteneciente a un operador de telecomunicaciones, quien provee conectividad entre las sedes utilizando la tecnología VPN MPLS con dos accesos diversificados entre sí desde cada una de las sedes del Contact Center. El resultado de esta contratación es el aprovechamiento de las ventajas que un operador de telecomunicaciones puede ofrecer a sus clientes, en relación a calidad de servicio, disponibilidad y expansión geográfica. De la misma manera, se definen una serie de criterios o niveles de servicio que aseguran a un Contact Center una comunicación de calidad entre sus sedes, entendiéndose por comunicación de calidad aquella que sea capaz de transmitirse con unos valores mínimos de pérdida de paquetes así como retraso en la transmisión, y una velocidad acorde a la demanda de los servicios de voz y datos. Como parte de la solución, se diseña una conexión redundante a Internet que proporciona acceso a todas las sedes del Contact Center. La solución de conectividad local en cada una de las sedes de un Contact Center se ha diseñado de manera general acorde al volumen de puestos de usuarios y escalabilidad que pueda tener cada una de las sedes. De esta manera se muestran varias opciones asociadas al equipamiento actual que ofrece el fabricante Cisco Systems, Inc.. Como parte de la solución se han definido los criterios de calidad para la elección de los Centros de Datos (Data Center). Un Contact Center tiene conexiones hacia o desde las empresas cliente a las que da servicio y provee de acceso a la red a sus tele-trabajadores. Este requerimiento junto con el acceso y servicios publicados en Internet necesita una infraestructura de seguridad. Este hecho da lugar al diseño de una solución que unifica todas las conexiones bajo una única infraestructura, dividiendo de manera lógica o virtual cada uno de los servicios. De la misma manera, se ha definido la utilización de protocolos como 802.1X para evitar accesos no autorizados a la red del Contact Center. La solución de voz elegida es heterogénea y capaz de soportar los protocolos de señalización más conocidos (SIP y H.323). De esta manera se busca tener la máxima flexibilidad para establecer enlaces de voz sobre IP (Trunk IP) con proveedores y clientes. Esto se logra gracias a la utilización de SBCs y a una infraestructura interna de voz basada en el fabricante Avaya Inc. Los sistemas de VoIP en un Contact Center son los elementos clave para poder realizar la prestación del servicio; por esta razón se elige una solución redundada bajo un entorno virtual. Esta solución permite desplegar el sistema de VoIP desde cualquiera de los Data Center del Contact Center. La solución llevada a cabo en este proyecto está principalmente basada en mi experiencia laboral adquirida durante los últimos siete años en el departamento de comunicaciones de una empresa de Contact Center. He tenido en cuenta los principales requerimientos que exigen hoy en día la mayor parte de empresas que desean contratar un servicio de Contact Center. Este proyecto está dividido en cuatro capítulos. El primer capítulo es una introducción donde se explican los principales escenarios de negocio y áreas técnicas necesarias para la prestación de servicios de Contact Center. El segundo capítulo describe de manera resumida, las principales tecnologías y protocolos que serán utilizados para llevar a cabo el diseño de la solución técnica de creación de una red de comunicaciones para una empresa de Contact Center. En el tercer capítulo se expone la solución técnica necesaria para permitir que una empresa de Contact Center preste sus servicios desde distintas ubicaciones distribuidas geográficamente, utilizando dos Data Centers donde se centralizan las aplicaciones de voz y datos. Finalmente, en el cuarto capítulo se presentan las conclusiones obtenidas tras la elaboración de la presente memoria, así como una propuesta de trabajos futuros, que permitirían junto con el proyecto actual, realizar una solución técnica completa incluyendo otras áreas tecnológicas necesarias en una empresa de Contact Center. Todas las ilustraciones y tablas de este proyecto son de elaboración propia a partir de mi experiencia profesional y de la información obtenida en diversos formatos de la bibliografía consultada, excepto en los casos en los que la fuente es mencionada. ABSTRACT This project shows a network solution for a company that provides Contact Center services from different locations geographically distributed, using the Telephone over Internet Protocol (ToIP) technology. The goal of this project is to become a design guide for performing network solutions using current communications equipment developed by the manufacturer Cisco Systems, Inc., firewalls developed by the manufacturer Fortinet and telephone systems developed by Avaya Inc. and Oracle Corporation, due to their great market reputation and their contributions that each one has made in the field of Contact Center. In order to provide interconnection between its different sites, the Contact Center needs to hire the services of a telecommunications’ operator, who will use the VPN MPLS technology, with two diversified access from each Contact Center’s site. The result of this hiring is the advantage of the benefits that a telecommunications operator can offer to its customers, regarding quality of service, availability and geographical expansion. Likewise, Service Level Agreement (SLA) has to be defined to ensure the Contact Center quality communication between their sites. A quality communication is understood as a communication that is capable of being transmitted with minimum values of packet loss and transmission delays, and a speed according to the demand for its voice and data services. As part of the solution, a redundant Internet connection has to be designed to provide access to every Contact Center’s site. The local connectivity solution in each of the Contact Center’s sites has to be designed according to its volume of users and scalability that each one may have. Thereby, the manufacturer Cisco Systems, Inc. offers several options associated with the current equipment. As part of the solution, quality criteria are being defined for the choice of the Data Centers. A Contact Center has connections to/from the client companies that provide network access to teleworkers. This requires along the access and services published on the Internet, needs a security infrastructure. Therefore is been created a solution design that unifies all connections under a single infrastructure, dividing each services in a virtual way. Likewise, is been defined the use of protocols, such as 802.1X, to prevent unauthorized access to the Contact Center’s network. The voice solution chosen is heterogeneous and capable of supporting best-known signaling protocols (SIP and H.323) in order to have maximum flexibility to establish links of Voice over IP (IP Trunk) with suppliers and clients. This can be achieved through the use of SBC and an internal voice infrastructure based on Avaya Inc. The VoIP systems in a Contact Center are the key elements to be able to provide the service; for this reason a redundant solution under virtual environment is been chosen. This solution allows any of the Data Centers to deploy the VoIP system. The solution carried out in this project is mainly based on my own experience acquired during the past seven years in the communications department of a Contact Center company. I have taken into account the main requirements that most companies request nowadays when they hire a Contact Center service. This project is divided into four chapters. The first chapter is an introduction that explains the main business scenarios and technical areas required to provide Contact Center services. The second chapter describes briefly the key technologies and protocols that will be used to carry out the design of the technical solution for the creation of a communications network in a Contact Center company. The third chapter shows a technical solution required that allows a Contact Center company to provide services from across geographically distributed locations, using two Data Centers where data and voice applications are centralized. Lastly, the fourth chapter includes the conclusions gained after making this project, as well as a future projects proposal, which would allow along the current project, to perform a whole technical solution including other necessary technologic areas in a Contact Center company All illustrations and tables of this project have been made by myself from my professional experience and the information obtained in various formats of the bibliography, except in the cases where the source is indicated.

Ubiquitous green computing techniques for high demand applications in smart environments

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.

GreenDisc: a HW/SW energy optimization framework in globally distributed computation

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.

GreenDisc: a HW/SW energy optimization framework in globally distributed computation

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.

On the economics of energy consumption in 4G networks: the case of Spain

Reducing energy consumption is one of the main challenges in most countries. For example, European Member States agreed to reduce greenhouse gas (GHG) emissions by 20% in 2020 compared to 1990 levels (EC 2008). Considering each sector separately, ICTs account nowadays for 2% of total carbon emissions. This percentage will increase as the demand of communication services and applications steps up. At the same time, the expected evolution of ICT-based developments - smart buildings, smart grids and smart transportation systems among others - could result in the creation of energy-saving opportunities leading to global emission reductions (Labouze et al. 2008), although the amount of these savings is under debate (Falch 2010). The main development required in telecommunication networks ?one of the three major blocks of energy consumption in ICTs together with data centers and consumer equipment (Sutherland 2009) ? is the evolution of existing infrastructures into ultra-broadband networks, the so-called Next Generation Networks (NGN). Fourth generation (4G) mobile communications are the technology of choice to complete -or supplement- the ubiquitous deployment of NGN. The risk and opportunities involved in NGN roll-out are currently in the forefront of the economic and policy debate. However, the issue of which is the role of energy consumption in 4G networks seems absent, despite the fact that the economic impact of energy consumption arises as a key element in the cost analysis of this type of networks. Precisely, the aim of this research is to provide deeper insight on the energy consumption involved in the usage of a 4G network, its relationship with network main design features, and the general economic impact this would have in the capital and operational expenditures related with network deployment and usage.

Enhancing regression models for complex systems using evolutionary techniques for feature engineering

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.

Server power modeling for run-time energy optimization of cloud computing facilities

As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. The average consumption of a single data center is equivalent to the energy consumption of 25.000 households. 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. This work proposes an automatic method, based on Multi-Objective Particle Swarm Optimization, for the identification of power models of enterprise servers in Cloud data centers. Our approach, as opposed to previous procedures, does not only consider the workload consolidation for deriving the power model, but also incorporates other non traditional factors like the static power consumption and its dependence with temperature. Our experimental results shows that we reach slightly better models than classical approaches, but simul- taneously simplifying the power model structure and thus the numbers of sensors needed, which is very promising for a short-term energy prediction. This work, validated with real Cloud applications, broadens the possibilities to derive efficient energy saving techniques for Cloud facilities.