Energy-Aware Cloud Management through Progressive SLA Specification

Novel energy-aware cloud management methods dynamically reallocate computation across geographically distributed data centers to leverage regional electricity price and temperature differences. As a result, a managed VM may suffer occasional downtimes. Current cloud providers only offer high availability VMs, without enough flexibility to apply such energy-aware management. In this paper we show how to analyse past traces of dynamic cloud management actions based on electricity prices and temperatures to estimate VM availability and price values. We propose a novel SLA specification approach for offering VMs with different availability and price values guaranteed over multiple SLAs to enable flexible energy-aware cloud management. We determine the optimal number of such SLAs as well as their availability and price guaranteed values. We evaluate our approach in a user SLA selection simulation using Wikipedia and Grid'5000 workloads. The results show higher customer conversion and 39% average energy savings per VM.Comment: 14 pages, conferenc

Exploiting heterogeneity in Chip-Multiprocessor Design

In the past decade, semiconductor manufacturers are persistent in building faster and smaller transistors in order to boost the processor performance as projected by Moore’s Law. Recently, as we enter the deep submicron regime, continuing the same processor development pace becomes an increasingly difficult issue due to constraints on power, temperature, and the scalability of transistors. To overcome these challenges, researchers propose several innovations at both architecture and device levels that are able to partially solve the problems. These diversities in processor architecture and manufacturing materials provide solutions to continuing Moore’s Law by effectively exploiting the heterogeneity, however, they also introduce a set of unprecedented challenges that have been rarely addressed in prior works. In this dissertation, we present a series of in-depth studies to comprehensively investigate the design and optimization of future multi-core and many-core platforms through exploiting heteroge-neities. First, we explore a large design space of heterogeneous chip multiprocessors by exploiting the architectural- and device-level heterogeneities, aiming to identify the optimal design patterns leading to attractive energy- and cost-efficiencies in the pre-silicon stage. After this high-level study, we pay specific attention to the architectural asymmetry, aiming at developing a heterogeneity-aware task scheduler to optimize the energy-efficiency on a given single-ISA heterogeneous multi-processor. An advanced statistical tool is employed to facilitate the algorithm development. In the third study, we shift our concentration to the device-level heterogeneity and propose to effectively leverage the advantages provided by different materials to solve the increasingly important reliability issue for future processors

Energy and performance-aware scheduling and shut-down models for efficient cloud-computing data centers.

This Doctoral Dissertation, presented as a set of research contributions, focuses on resource efficiency in data centers. This topic has been faced mainly by the development of several energy-efficiency, resource managing and scheduling policies, as well as the simulation tools required to test them in realistic cloud computing environments. Several models have been implemented in order to minimize energy consumption in Cloud Computing environments. Among them: a) Fifteen probabilistic and deterministic energy-policies which shut-down idle machines; b) Five energy-aware scheduling algorithms, including several genetic algorithm models; c) A Stackelberg game-based strategy which models the concurrency between opposite requirements of Cloud-Computing systems in order to dynamically apply the most optimal scheduling algorithms and energy-efficiency policies depending on the environment; and d) A productive analysis on the resource efficiency of several realistic cloud–computing environments. A novel simulation tool called SCORE, able to simulate several data-center sizes, machine heterogeneity, security levels, workload composition and patterns, scheduling strategies and energy-efficiency strategies, was developed in order to test these strategies in large-scale cloud-computing clusters. As results, more than fifty Key Performance Indicators (KPI) show that more than 20% of energy consumption can be reduced in realistic high-utilization environments when proper policies are employed.Esta Tesis Doctoral, que se presenta como compendio de artículos de investigación, se centra en la eficiencia en la utilización de los recursos en centros de datos de internet. Este problema ha sido abordado esencialmente desarrollando diferentes estrategias de eficiencia energética, gestión y distribución de recursos, así como todas las herramientas de simulación y análisis necesarias para su validación en entornos realistas de Cloud Computing. Numerosas estrategias han sido desarrolladas para minimizar el consumo energético en entornos de Cloud Computing. Entre ellos: 1. Quince políticas de eficiencia energética, tanto probabilísticas como deterministas, que apagan máquinas en estado de espera siempre que sea posible; 2. Cinco algoritmos de distribución de tareas que tienen en cuenta el consumo energético, incluyendo varios modelos de algoritmos genéticos; 3. Una estrategia basada en la teoría de juegos de Stackelberg que modela la competición entre diferentes partes de los centros de datos que tienen objetivos encontrados. Este modelo aplica dinámicamente las estrategias de distribución de tareas y las políticas de eficiencia energética dependiendo de las características del entorno; y 4. Un análisis productivo sobre la eficiencia en la utilización de recursos en numerosos escenarios de Cloud Computing. Una nueva herramienta de simulación llamada SCORE se ha desarrollado para analizar las estrategias antes mencionadas en clústers de Cloud Computing de grandes dimensiones. Los resultados obtenidos muestran que se puede conseguir un ahorro de energía superior al 20% en entornos realistas de alta utilización si se emplean las estrategias de eficiencia energética adecuadas. SCORE es open source y puede simular diferentes centros de datos con, entre otros muchos, los siguientes parámetros: Tamaño del centro de datos; heterogeneidad de los servidores; tipo, composición y patrones de carga de trabajo, estrategias de distribución de tareas y políticas de eficiencia energética, así como tres gestores de recursos centralizados: Monolítico, Two-level y Shared-state. Como resultados, esta herramienta de simulación arroja más de 50 Key Performance Indicators (KPI) de rendimiento general, de distribucin de tareas y de energía.Premio Extraordinario de Doctorado U

Energy Saving and Scavenging in Stand-alone and Large Scale Distributed Systems.

This thesis focuses on energy management techniques for distributed systems such as hand-held mobile devices, sensor nodes, and data center servers. One of the major design problems in multiple application domains is the mismatch between workloads and resources. Sub-optimal assignment of workloads to resources can cause underloaded or overloaded resources, resulting in performance degradation or energy waste. This work specifically focuses on the heterogeneity in system hardware components and workloads. It includes energy management solutions for unregulated or batteryless embedded systems; and data center servers with heterogeneous workloads, machines, and processor wear states. This thesis describes four major contributions: (1) This thesis describes a battery test and energy delivery system design process to maintain battery life in embedded systems without voltage regulators. (2) In battery-less sensor nodes, this thesis demonstrates a routing protocol to maintain reliable transmission through the sensor network. (3) This thesis has characterized typical workloads and developed two models to capture the heterogeneity of data center tasks and machines: a task performance model and a machine resource utilization model. These models allow users to predict task finish time on individual machines. It then integrates these two models into a task scheduler based on the Hadoop framework for MapReduce tasks, and uses this scheduler for server energy minimization using task concentration. (4) In addition to saving server energy consumption, this thesis describes a method of reducing data center cooling energy by maintaining optimal server processor temperature setpoints through a task assignment algorithm. This algorithm considers the reliability impact of processor wear states. It records processor wear states through automatic timing slack tests on a cluster of machines with varying core temperatures, voltages, and frequencies. These optimal temperature setpoints are used in a task scheduling algorithm that saves both server and cooling energy.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116746/1/xjhe_1.pd

insights into the effects of occupant behaviour lifestyles and building automation on building energy use

Abstract In order to optimize building energy consumption, Member States will have to establish minimum efficiency requirements for systems, and promote the introduction of active control system in new constructions or major renovations. Energy saving, plant efficiency and environmental sustainability are also factors delineating smart buildings. Interestingly, occupant behaviour is known to be one of the key sources of uncertainty in the prediction of building energy use. The success of automation strategies is recognized to be dependent on how the occupants interact with the building. The present research describes the effect of different building occupants' lifestyles and building automation on a high performing building

Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

Task Activity Vectors: A Novel Metric for Temperature-Aware and Energy-Efficient Scheduling

This thesis introduces the abstraction of the task activity vector to characterize applications by the processor resources they utilize. Based on activity vectors, the thesis introduces scheduling policies for improving the temperature distribution on the processor chip and for increasing energy efficiency by reducing the contention for shared resources of multicore and multithreaded processors

Study, analysis and new scheduling proposals in partitioned real-time systems

[ES] En nuestra vida cotidiana, cada vez más ordenadores controlan nuestro entorno: teléfonos móviles, procesos industriales, asistencia a la conducción, etc. Todos estos sistemas presentan requisitos estrictos para garantizar un comportamiento adecuado. En muchos de estos sistemas, cumplir con las restricciones de tiempo es un factor tan importante como el resultado lógico de los cálculos. Desde hace aproximadamente 40 años, los sistemas en tiempo real son muy atractivos en el campo de la computación y hoy en día se aplican en áreas de gran alcance como aplicaciones industriales, aplicaciones aeroespaciales, telecomunicaciones, electrónica de consumo, etc. Algunos retos a abordar en el campo del tiempo real son el determinismo y la predecibilidad del comportamiento temporal del sistema. En este sentido, garantizar la ejecución del programa y los tiempos de respuesta del sistema son requisitos esenciales que deben cumplirse estrictamente a través de estrategias apropiadas de planificación de tareas. Además, las arquitecturas multiprocesador se están volviendo más populares debido al hecho de que las capacidades de procesamiento y los recursos computacionales de los sistemas están aumentando. Un estudio reciente estima que existe una tendencia creciente entre las arquitecturas multiprocesador a combinar diferentes niveles de criticidad en el mismo sistema. En este sentido, proporcionar aislamiento entre las aplicaciones es extremadamente necesario. La tecnología particionada es capaz de lidiar con este propósito. Además, la gestión de la energía es un problema relevante en los sistemas en tiempo real. Muchos sistemas empotrados de tiempo real, como dispositivos portátiles o robots móviles que requieren baterías, buscan encontrar técnicas que reduzcan el consumo de energía y, como consecuencia, aumenten la vida útil de sus baterías. También se obtienen claros beneficios operativos, financieros, monetarios y ambientales al minimizar el consumo de energía. Con todo ello, este trabajo aborda el problema de planificabilidad y contribuye al estudio de las nuevas técnicas de planificación en sistemas particionados de tiempo real. Estas técnicas proporcionan el tiempo mínimo para planificar de manera factible conjuntos de tareas. Además, se proponen técnicas de asignación para sistemas multiprocesador cuyo objetivo principal es reducir el consumo de energía del sistema global. Finalmente, se presentan los resultados obtenidos así como los trabajos futuros relacionados con este trabajo[CA] En la nostra vida quotidiana, cada vegada més ordenadors controlen el nostre entorn: telèfons mòbils, processos industrials, assistència a la conducció, etc. Tots aquests sistemes presenten requisits estrictes per a garantir un comportament adequat. En molts d' aquests sistemes, complir amb les restriccions de temps és un factor tan important com el resultat lògic dels càlculs. Des de fa aproximadament 40 anys, els sistemes en temps real són molt atractius en el camp de la computació i hui dia s' apliquen en àrees de gran abast com a aplicacions industrials, aplicacions aeroespacials, telecomunicacions, electrònica de consum, etc. Alguns reptes a abordar en el camp del temps real són el determinisme i la predictibilitat del comportament temporal del sistema. En aquest sentit, garantir l'execució del programa i els temps de resposta del sistema són requisits essencials que han de complir-se estrictament a través d'estratègies apropiades de planificació de tasques. A més, les arquitectures multiprocessador s'estan tornant més populars a causa del fet que les capacitats de processament i els recursos computacionals dels sistemes estan augmentant. Un estudi recent estima que existeix una tendència creixent entre les arquitectures multiprocessador a combinar diferents nivells de criticitat en el mateix sistema. En aquest sentit, proporcionar aïllament entre les aplicacions és extremadament necessari. La tecnologia particionada és capaç de bregar amb aquest propòsit. A més, la gestió de l'energia és un problema rellevant en els sistemes en temps real. Molts sistemes embebits de temps real, com a dispositius portàtils o robots mòbils que requereixen bateries, busquen trobar tècniques que reduïsquen el consum d'energia i, com a conseqüència, augmenten la vida útil de les seues bateries. També s'obtenen clars beneficis operatius, financers, monetaris i ambientals en minimitzar el consum d'energia. Amb tot això, aquest treball aborda el problema de planificabilitat i contribueix a l'estudi de les noves tècniques de planificació en sistemes particionats de temps real. Aquestes tècniques proporcionen el temps mínim per a planificar de manera factible conjunts de tasques. A més, es proposen tècniques d'assignació per a sistemes multiprocessador l'objectiu principal del qual és reduir el consum d'energia del sistema global. Finalment, es presenten els resultats obtinguts així com els treballs futurs relacionats amb aquest treball.[EN] In our everyday lives, more and more computers are controlling our environment: mobile phones, industrial processes, driving assistance, etc. All these systems present strict requirements to ensure proper behaviour. In many of these systems, the time at which the action is delivered is as important as the logical result of the computation. About 40 years ago, real-time systems began to attract attention in computing field and nowadays are applied in wide ranging areas as industrial applications, aerospace, telecommunication applications, consumer electronics, etc. Some real-time challenges that must be addressed are determinism and predictability of the temporal behaviour of the system. In this sense, to guarantee program execution and system response times are essential requirements that must be strictly met through appropriate task scheduling strategies. Furthermore, multiprocessor architectures are becoming more popular due to the fact that processing capabilities and computational resources are increasing. A recent study estimates that there is an increasing tendency among multiprocessor architectures to combine different levels of criticality in the same system. In this sense, to provide isolation between applications is extremely required. Partitioned technology is able to deal with this purpose. In addition, energy management is a relevant problem in real-time systems. Many real-time embedded systems, as wearable devices or mobile robots that require batteries, seek to find techniques that reduce the energy consumption and, as a consequence, increase the lifetime of their batteries. Also clear operational, financial, monetary and environmental gains are reached when minimizing energy consumption. Faced with all this, this work addresses the problem of schedulability and contributes to the study of new scheduling techniques in partitioned real-time systems. These techniques provide the minimum time to feasible schedule tasks sets. Moreover, allocation techniques for multicore systems whose main objective is to reduce the energy consumption of the overall system are also proposed. Finally, some of the obtained results are discussed as conclusions and future works are introduced.Guasque Ortega, A. (2019). Study, analysis and new scheduling proposals in partitioned real-time systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/135279TESI