Hacia la conciencia social del consumo energético en centros de datos

Ante el problema creciente del consumo en los centros de datos, unido a la adopción paulatina de las mejores prácticas actuales para mejorar la eficiencia energética, se hace imprescindible un cambio radical en el enfoque de la energía en dichos centros de datos para poder seguir reduciendo de manera significativa su impacto medioambiental. En este artículo presentamos una propuesta inicial para la optimización integral del consumo de energía en centros de datos, que ha sido validado en un escenario de monitorización poblacional de salud, con ahorros de hasta un 50% frente al estado del arte en eficiencia energética. Defendemos una conciencia global del Estado y el comportamiento térmico del centro de datos, utilizando modelos predictivos para anticipar las variables determinantes para la optimización. Además, las estrategias de optimización energética de los centros de datos del futuro tienen que ser sociales: los distintos elementos (servidores, software de gestión, sistemas de refrigeración) deben tener cierta conciencia del estado de los demás elementos del sistema y de cómo el entorno los puede perjudicar o favorecer, buscando el consenso en estrategias colaborativas para reducir el consumo total

The DEVStone Metric: Performance Analysis of DEVS Simulation Engines

The DEVStone benchmark allows us to evaluate the performance of discrete-event simulators based on the DEVS formalism. It provides model sets with different characteristics, enabling the analysis of specific issues of simulation engines. However, this heterogeneity hinders the comparison of the results among studies, as the results obtained on each research work depend on the chosen subset of DEVStone models. We define the DEVStone metric based on the DEVStone synthetic benchmark and provide a mechanism for specifying objective ratings for DEVS-based simulators. This metric corresponds to the average number of times that a simulator can execute a selection of 12 DEVStone models in one minute. The variety of the chosen models ensures we measure different particularities provided by DEVStone. The proposed metric allows us to compare various simulators and to assess the impact of new features on their performance. We use the DEVStone metric to compare some popular DEVS-based simulators

Sustainable Edge Computing: Challenges and Future Directions

An increasing amount of data is being injected into the network from IoT (Internet of Things) applications. Many of these applications, developed to improve society's quality of life, are latency-critical and inject large amounts of data into the network. These requirements of IoT applications trigger the emergence of Edge computing paradigm. Currently, data centers are responsible for a global energy use between 2% and 3%. However, this trend is difficult to maintain, as bringing computing infrastructures closer to the edge of the network comes with its own set of challenges for energy efficiency. In this paper, we propose our approach for the sustainability of future computing infrastructures to provide (i) an energy-efficient and economically viable deployment, (ii) a fault-tolerant automated operation, and (iii) a collaborative resource management to improve resource efficiency. We identify the main limitations of applying Cloud-based approaches close to the data sources and present the research challenges to Edge sustainability arising from these constraints. We propose two-phase immersion cooling, formal modeling, machine learning, and energy-centric federated management as Edge-enabling technologies. We present our early results towards the sustainability of an Edge infrastructure to demonstrate the benefits of our approach for future computing environments and deployments.Comment: 26 pages, 16 figure

On the leakage-power modeling for optimal server operation

Leakage power consumption is a com- ponent of the total power consumption in data cen- ters 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 re- liability 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 data- center can be optimized by a reduction of the cooling budget

A novel energy-driven computing paradigm for e-health scenarios

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

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

Runtime data center temperature prediction using Grammatical Evolution techniques

Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEMinisterio de Economía y Competitividad (MINECO)pu

xDEVS: A toolkit for interoperable modeling and simulation of formal discrete event systems

Employing Modeling and Simulation (M&S) extensively to analyze and develop complex systems is the norm today. The use of robust M&S formalisms and rigorous methodologies is essential to deal with complexity. Among them, the Discrete Event System Specification (DEVS) provides a solid framework for modeling structural, behavior and information aspects of any complex system. This gives several advantages to analyze and design complex systems: completeness, verifiability, extensibility, and maintainability. DEVS formalism has been implemented in many programming languages and executable on multiple platforms. In this paper, we describe the features of an M&S framework called xDEVS that builds upon the prevalent DEVS Application Programming Interface (API) for both modeling and simulation layers, promoting interoperability between the existing platform-specific (C++, Java, Python) DEVS implementations. Additionally, the framework can simulate the same model using sequential, parallel, or distributed architectures. The M&S engine has been reinforced with several strategies to improve performance, as well as tools to perform model analysis and verification. Finally, xDEVS also facilitates systems engineers to apply the vision of model-based systems engineering (MBSE), model-driven engineering (MDE), and model-driven systems engineering (MDSE) paradigms. We highlight the features of the proposed xDEVS framework with multiple examples and case studies illustrating the rigor and diversity of application domains it can support

Self-Organizing maps for detecting abnormal thermal behavior in data centers

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

El arenero: un recurso didáctico para el desarrollo de la motricidad gruesa en la educación inicial

The Sandbox is a didactic resource for the development of gross motor skills in early-age children, enhancing capacities, abilities and skills that would later become competencies. The smaller the child is, the better he assimilates the conceptions of his context through the internalization of the parts of his body. Sand is a tactile and stimulating element, which, adapted in the educational field, becomes a didactic resource for the experimentation of children, where they can play, socialize, build, design, helping the development of their motor skills and creativity. Objective. The study aims to analyze the importance of using the sandbox in the development of children's gross motor skills. Methodology, a mixed methodology is applied, it is qualitative because the results of the research were entered into a criteria analysis to support the theoretical framework, and quantitative because data and results obtained scientifically in a numerical way were processed. Conclusion. Finally, it is concluded that it is important to carry out playful activities inside the sandbox since they still contribute to the development of gross motor skills, especially in the initial age, since different body areas are stimulated through play, awakening the interest of children.El Arenero constituye un recurso didáctico para el desarrollo de la motricidad gruesa de los niños en edad inicial potenciando capacidades, habilidades y destrezas que posteriormente se convertían en competencias. Cuánto más pequeño es el niño asimila de mejor manera las concepciones de su contexto por medio de la interiorización de las partes de su cuerpo. La arena es un elemento táctil y estimulante, que, adaptado en el ámbito educativo se transforma en un recurso didáctico para la experimentación de los niños, en donde pueden jugar, socializar, construir, diseñar, ayudando al desarrollo de su motricidad y creatividad. Objetivo. El estudio tiene por objetivo analizar la importancia del uso del arenero en el desarrollo de la motricidad gruesa de los niños. Metodología, se aplica una metodología mixta, es de carácter cualitativo porque los resultados de la investigación fueron ingresados a un análisis de criterios como apoyo al marco teórico, y cuantitativa porque se procesó datos y resultados obtenidos de manera científica en forma numérica. Conclusión. Finalmente se concluye que es importante realizar actividades lúdicas dentro del arenero puesto que éstas coadyuvan al desarrollo de la motricidad gruesa especialmente en la edad inicial pues mediante el juego se estimulan diferentes áreas corporales despertando el interés por parte de los niños/as