33 research outputs found

    Towards Management of Energy Consumption in HPC Systems with Fault Tolerance

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    High-performance computing continues to increase its computing power and energy efficiency. However, energy consumption continues to rise and finding ways to limit and/or decrease it is a crucial point in current research. For high-performance MPI applications, there are rollback recovery based fault tolerance methods, such as uncoordinated checkpoints. These methods allow only some processes to go back in the face of failure, while the rest of the processes continue to run. In this article, we focus on the processes that continue execution, and propose a series of strategies to manage energy consumption when a failure occurs and uncoordinated checkpoints are used. We present an energy model to evaluate strategies and through simulation we analyze the behavior of an application under different configurations and failure time. As a result, we show the feasibility of improving energy efficiency in HPC systems in the presence of a failure.Instituto de Investigación en InformáticaComisión de Investigaciones Científicas de la provincia de Buenos Aire

    Energy efficiency in wireless communications for mobile user devices

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    Mención Internacional en el título de doctorMobile user devices’ market has experi-enced an exponential growth worldwide over the last decade, and wireless communications are the main driver for the next generation of 5G networks. The ubiquity of battery-powered connected devices makes energy efficiency a major research issue. While most studies assumed that network interfaces dominate the energy consumption of wireless communications, a recent work unveils that the frame processing carried out by the device could drain as much energy as the interface itself for many devices. This discovery poses doubts on prior energy models for wireless communications and forces us to reconsider existing energy-saving schemes. From this standpoint, this thesis is de-voted to the study of the energy efficiency of mobile user devices at multiple layers. To that end, we assemble a comprehensive en-ergy measurement framework, and a robust methodology, to be able to characterise a wide range of mobile devices, as well as individual parts of such devices. Building on this, we first delve into the en-ergy consumption of frame processing within the devices’ protocol stack. Our results identify the CPU as the leading cause of this energy consumption. Moreover, we discover that the characterisation of the energy toll ascribed to the device is much more complex than the previous work showed. Devices with complex CPUs (several frequencies and sleep states) require novel methodologies and models to successfully characterise their consumption. We then turn our attention to lower levels of the communication stack by investigating the behaviour of idle WiFi interfaces. Due to the design of the 802.11 protocol, together with the growing trend of network densification, WiFi devices spend a long time receiving frames addressed to other devices when they might be dormant. In order to mitigate this issue, we study the timing constraints of a commercial WiFi card, which is developed into a standard-compliant algorithm that saves energy during such transmissions. At a higher level, rate adaptation and power control techniques adapt data rate and output power to the channel conditions. However, these have been typically studied with other metrics rather than energy efficiency in mind (i.e., performance figures such as throughput and capacity). In fact, our analyses and sim-ulations unveil an inherent trade-off between throughput and energy efficiency maximisa-tion in 802.11. We show that rate adaptation and power control techniques may incur inef-ficiencies at mode transitions, and we provide energy-aware heuristics to make such decisions following a conservative approach. Finally, our research experience on simula-tion methods pointed us towards the need for new simulation tools commited to the middle-way approach: less specificity than complex network simulators in exchange for easier and faster prototyping. As a result, we developed a process-oriented and trajectory-based discrete-event simulation package for the R language, which is designed as a easy-to-use yet pow-erful framework with automatic monitoring capabilities. The use of this simulator in net-working is demonstrated through the energy modelling of an Internet-of-Things scenario with thousands of metering devices in just a few lines of code.El mercado de los dispositivos de usuario móviles ha experimentado un crecimiento exponencial a nivel mundial en la última década, y las comunicaciones inalámbricas son el principal motor de la siguiente generación de redes 5G. La ubicuidad de estos dispos-itivos alimentados por baterías hace de la eficiencia energética un importante tema de investigación. Mientras muchos estudios asumían que la interfaz de red domina el consumo energético de las comuni-caciones inalámbricas, un trabajo reciente revela que el procesado de tramas que se lleva a cabo en el disposi-tivo podría gastar tanta energía como la propia interfaz para muchos dispositivos. Este descubrimiento plantea dudas sobre los anteriores modelos energéticos para comunicaciones inalámbricas y nos obliga a reconsid-erar los esquemas de ahorro energético existentes. Desde este punto de vista, esta tesis está dedicada al estudio de la eficiencia energética de dispositivos de usuario móviles en múltiples capas. Para ello, se construye un completo sistema de medida de energía, y una metodología robusta, capaz de caracterizar un amplio rango de dispositivos móviles, así como partes individuales de tales dispositivos. A partir de esto, en primer lugar se profundiza en el consumo energético del procesamiento de tramas en la pila de protocolos de los dispositivos. Nuestros resul-tados identifican a la CPU como principal causa de tal consumo. Además, se descubre que la caracterización de la cuota energética adscrita al dispositivo es mucho más compleja que lo mostrado por el trabajo ante-rior. Los dispositivos con CPU complejas (múltiples frecuencias y modos de apagado) requieren nuevas metodologías y modelos para caracterizar su consumo de manera existosa. En este punto, volvemos nuestra atención hacia niveles más bajos de la pila de comunicaciones para investigar el comportamiento de las interfaces WiFi en estado inactivo. Debido al diseño del protocolo 802.11, junto con la tendencia creciente hacia la densifi-cación de las redes, los dispositivos WiFi pasan mucho tiempo recibiendo tramas destinadas a otros dispos-itivos cuando podrían estar apagados. Para mitigar este problema, se estudian las limitaciones temporales de una tarjeta WiFi comercial, lo que posteriormente se utiliza para desarrollar un algoritmo conforme con el estándar que es capaz de ahorrar energía durante dichas transmisiones. A un nivel más alto, las técnicas de adaptación de tasa y control de potencia adaptan la tasa de datos y la potencia de salida a las condiciones del canal. No obstante, estas técnicas han sido típicamente es-tudiadas con otras métricas en mente (i.e., figuras de rendimiento como la tasa total y la capacidad). De hecho, nuestros análisis y simulaciones desvelan un conflicto entre la maximización de la tasa total y la efi-ciencia energética en 802.11. Se muestra que las técni-cas de adaptación de tasa y control de potencia pueden incurrir en ineficiencias en los cambios de modo, y se proporcionan heurísticos para tomar tales decisiones de un modo conservador y eficiente energéticamente. Finalmente, nuestra experiencia investigadora en métodos de simulación nos hizo conscientes de la necesidad de nuevas herramientas de simulación comprometidas con un enfoque intermedio: menos especificidad que los complejos simuladores de re-des a cambio de facilidad y rapidez en el prototipado. Como resultado, se desarrolló un paquete de simu-lación por eventos discretos para el lenguaje R orien-tado a procesos y basado en trayectorias, el cual está diseñado como una herramienta fácil de utilizar a la par que potente con capacidad de monitorización au-tomática integrada. El uso de este simulador en redes se demuestra mediante el modelado en energía de un escenario de la Internet de las Cosas con miles de dis-positivos de medida en tan solo unas pocas líneas de código.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Juan Manuel López Soler.- Secretario: Francisco Valera Pintor.- Vocal: Paul Horatiu Patra

    Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

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    This thesis presents an approach for the design time analysis of energy efficiency for static and self-adaptive software systems. The quality characteristics of a software system, such as performance and operating costs, strongly depend upon its architecture. Software architecture is a high-level view on software artifacts that reflects essential quality characteristics of a system under design. Design decisions made on an architectural level have a decisive impact on the quality of a system. Revising architectural design decisions late into development requires significant effort. Architectural analyses allow software architects to reason about the impact of design decisions on quality, based on an architectural description of the system. An essential quality goal is the reduction of cost while maintaining other quality goals. Power consumption accounts for a significant part of the Total Cost of Ownership (TCO) of data centers. In 2010, data centers contributed 1.3% of the world-wide power consumption. However, reasoning on the energy efficiency of software systems is excluded from the systematic analysis of software architectures at design time. Energy efficiency can only be evaluated once the system is deployed and operational. One approach to reduce power consumption or cost is the introduction of self-adaptivity to a software system. Self-adaptive software systems execute adaptations to provision costly resources dependent on user load. The execution of reconfigurations can increase energy efficiency and reduce cost. If performed improperly, however, the additional resources required to execute a reconfiguration may exceed their positive effect. Existing architecture-level energy analysis approaches offer limited accuracy or only consider a limited set of system features, e.g., the used communication style. Predictive approaches from the embedded systems and Cloud Computing domain operate on an abstraction that is not suited for architectural analysis. The execution of adaptations can consume additional resources. The additional consumption can reduce performance and energy efficiency. Design time quality analyses for self-adaptive software systems ignore this transient effect of adaptations. This thesis makes the following contributions to enable the systematic consideration of energy efficiency in the architectural design of self-adaptive software systems: First, it presents a modeling language that captures power consumption characteristics on an architectural abstraction level. Second, it introduces an energy efficiency analysis approach that uses instances of our power consumption modeling language in combination with existing performance analyses for architecture models. The developed analysis supports reasoning on energy efficiency for static and self-adaptive software systems. Third, to ease the specification of power consumption characteristics, we provide a method for extracting power models for server environments. The method encompasses an automated profiling of servers based on a set of restrictions defined by the user. A model training framework extracts a set of power models specified in our modeling language from the resulting profile. The method ranks the trained power models based on their predicted accuracy. Lastly, this thesis introduces a systematic modeling and analysis approach for considering transient effects in design time quality analyses. The approach explicitly models inter-dependencies between reconfigurations, performance and power consumption. We provide a formalization of the execution semantics of the model. Additionally, we discuss how our approach can be integrated with existing quality analyses of self-adaptive software systems. We validated the accuracy, applicability, and appropriateness of our approach in a variety of case studies. The first two case studies investigated the accuracy and appropriateness of our modeling and analysis approach. The first study evaluated the impact of design decisions on the energy efficiency of a media hosting application. The energy consumption predictions achieved an absolute error lower than 5.5% across different user loads. Our approach predicted the relative impact of the design decision on energy efficiency with an error of less than 18.94%. The second case study used two variants of the Spring-based community case study system PetClinic. The case study complements the accuracy and appropriateness evaluation of our modeling and analysis approach. We were able to predict the energy consumption of both variants with an absolute error of no more than 2.38%. In contrast to the first case study, we derived all models automatically, using our power model extraction framework, as well as an extraction framework for performance models. The third case study applied our model-based prediction to evaluate the effect of different self-adaptation algorithms on energy efficiency. It involved scientific workloads executed in a virtualized environment. Our approach predicted the energy consumption with an error below 7.1%, even though we used coarse grained measurement data of low accuracy to train the input models. The fourth case study evaluated the appropriateness and accuracy of the automated model extraction method using a set of Big Data and enterprise workloads. Our method produced power models with prediction errors below 5.9%. A secondary study evaluated the accuracy of extracted power models for different Virtual Machine (VM) migration scenarios. The results of the fifth case study showed that our approach for modeling transient effects improved the prediction accuracy for a horizontally scaling application. Leveraging the improved accuracy, we were able to identify design deficiencies of the application that otherwise would have remained unnoticed

    Energy Efficient Servers

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    Computer scienc

    Performance evaluation of virtual machine live migration for energy efficient large-scale computing

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    Ph. D. Thesis. (Integrated)Large-scale computing systems must overcome a number of di culties before they can be considered a long-term solution to information technology (IT) demands, including issues with power use and its green impact. Increasing the energy e ciency of largescale computing systems has long posed a challenge to researchers. Innovations in e cient energy use are needed that can lower energy costs and reduce the CO2 emissions associated with information and communications technology (ICT) equipment. For the purpose of facilitating energy e ciency in large-scale computing systems, virtual machine (VM) consolidation is among the key strategic approaches that can be employed. Virtual machine (VM) live migration has become an established technology used to consolidate virtualised workload onto a smaller number of physical machines, as a mechanism to reduce overall energy consumption. Nevertheless, it is important to acknowledge that the costs associated with VM live migration are not taken into account in the context of certain VM consolidation techniques. Organisations often exploit idle time on existing local computing infrastructure through High Throughput Computing (HTC) to perform the computation. More recently the same approach has been employed to make use of cloud resources in large-scale computation. To date, the impact of HTC scheduling policies within such environments has received limited attention in the literature as well as the trade-o between energy consumption and performance. Also, the bene ts of using virtualisation and live migration are not commonly applied in High Throughput Computing (HTC) environments. In this thesis, we illustrate through trace-driven simulation the trade-o between energy consumption and system performance for a number of HTC scheduling policies. Furthermore, the thesis demonstrates the way in which various workloads can a ect the time of VM live migration. We use a real experiment to explore the relation between various workload characteristics and the time of VM live migration. In order to understand what factors in uence live migration, we investigate three machine learning models to predict successful live migration using di erent training and evaluation - vii - sets drawn from our experimental data. Through this thesis, we explore how virtualisation and live migration can be employed in HTC environment and used as a fault-tolerance mechanism to reduce energy consumption and increase the utilisation of a single computer in a large computing infrastructure. We propose various migration policies and evaluate them through the use of our extensions to HTC-Sim simulation framework. Moreover, we compare the results between the policies as well as the system where migration is not considered. We demonstrate that our responsive migration could save approximately 75% of the system wasted energy due to job evictions by user interruptions where migration is not employed as a fault-tolerance mechanism

    Energy Efficient Servers

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    Computer scienc

    Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

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    This work presents an approach for the architecture analysis of energy efficiency for static and self-adaptive software systems. It introduces a modeling language that captures consumption characteristics on an architectural level. The outlined analysis predicts the energy efficiency of systems described with this language. Lastly, this work introduces an approach for considering transient effects in design time architecture analyses

    Energy-efficient Transitional Near-* Computing

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    Studies have shown that communication networks, devices accessing the Internet, and data centers account for 4.6% of the worldwide electricity consumption. Although data centers, core network equipment, and mobile devices are getting more energy-efficient, the amount of data that is being processed, transferred, and stored is vastly increasing. Recent computer paradigms, such as fog and edge computing, try to improve this situation by processing data near the user, the network, the devices, and the data itself. In this thesis, these trends are summarized under the new term near-* or near-everything computing. Furthermore, a novel paradigm designed to increase the energy efficiency of near-* computing is proposed: transitional computing. It transfers multi-mechanism transitions, a recently developed paradigm for a highly adaptable future Internet, from the field of communication systems to computing systems. Moreover, three types of novel transitions are introduced to achieve gains in energy efficiency in near-* environments, spanning from private Infrastructure-as-a-Service (IaaS) clouds, Software-defined Wireless Networks (SDWNs) at the edge of the network, Disruption-Tolerant Information-Centric Networks (DTN-ICNs) involving mobile devices, sensors, edge devices as well as programmable components on a mobile System-on-a-Chip (SoC). Finally, the novel idea of transitional near-* computing for emergency response applications is presented to assist rescuers and affected persons during an emergency event or a disaster, although connections to cloud services and social networks might be disturbed by network outages, and network bandwidth and battery power of mobile devices might be limited
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