Energy-aware scheduling in distributed computing systems

Distributed computing systems, such as data centers, are key for supporting modern computing demands. However, the energy consumption of data centers has become a major concern over the last decade. Worldwide energy consumption in 2012 was estimated to be around 270 TWh, and grim forecasts predict it will quadruple by 2030. Maximizing energy efficiency while also maximizing computing efficiency is a major challenge for modern data centers. This work addresses this challenge by scheduling the operation of modern data centers, considering a multi-objective approach for simultaneously optimizing both efficiency objectives. Multiple data center scenarios are studied, such as scheduling a single data center and scheduling a federation of several geographically-distributed data centers. Mathematical models are formulated for each scenario, considering the modeling of their most relevant components such as computing resources, computing workload, cooling system, networking, and green energy generators, among others. A set of accurate heuristic and metaheuristic algorithms are designed for addressing the scheduling problem. These scheduling algorithms are comprehensively studied, and compared with each other, using statistical tools to evaluate their efficacy when addressing realistic workloads and scenarios. Experimental results show the designed scheduling algorithms are able to significantly increase the energy efficiency of data centers when compared to traditional scheduling methods, while providing a diverse set of trade-off solutions regarding the computing efficiency of the data center. These results confirm the effectiveness of the proposed algorithmic approaches for data center infrastructures.Los sistemas informáticos distribuidos, como los centros de datos, son clave para satisfacer la demanda informática moderna. Sin embargo, su consumo de energético se ha convertido en una gran preocupación. Se estima que mundialmente su consumo energético rondó los 270 TWh en el año 2012, y algunos prevén que este consumo se cuadruplicará para el año 2030. Maximizar simultáneamente la eficiencia energética y computacional de los centros de datos es un desafío crítico. Esta tesis aborda dicho desafío mediante la planificación de la operativa del centro de datos considerando un enfoque multiobjetivo para optimizar simultáneamente ambos objetivos de eficiencia. En esta tesis se estudian múltiples variantes del problema, desde la planificación de un único centro de datos hasta la de una federación de múltiples centros de datos geográficmentea distribuidos. Para esto, se formulan modelos matemáticos para cada variante del problema, modelado sus componentes más relevantes, como: recursos computacionales, carga de trabajo, refrigeración, redes, energía verde, etc. Para resolver el problema de planificación planteado, se diseñan un conjunto de algoritmos heurísticos y metaheurísticos. Estos son estudiados exhaustivamente y su eficiencia es evaluada utilizando una batería de herramientas estadísticas. Los resultados experimentales muestran que los algoritmos de planificación diseñados son capaces de aumentar significativamente la eficiencia energética de un centros de datos en comparación con métodos tradicionales planificación. A su vez, los métodos propuestos proporcionan un conjunto diverso de soluciones con diferente nivel de compromiso respecto a la eficiencia computacional del centro de datos. Estos resultados confirman la eficacia del enfoque algorítmico propuesto

Enhancing Power Efficient Design Techniques in Deep Submicron Era

Excessive power dissipation has been one of the major bottlenecks for design and manufacture in the past couple of decades. Power efficient design has become more and more challenging when technology scales down to the deep submicron era that features the dominance of leakage, the manufacture variation, the on-chip temperature variation and higher reliability requirements, among others. Most of the computer aided design (CAD) tools and algorithms currently used in industry were developed in the pre deep submicron era and did not consider the new features explicitly and adequately. Recent research advances in deep submicron design, such as the mechanisms of leakage, the source and characterization of manufacture variation, the cause and models of on-chip temperature variation, provide us the opportunity to incorporate these important issues in power efficient design. We explore this opportunity in this dissertation by demonstrating that significant power reduction can be achieved with only minor modification to the existing CAD tools and algorithms. First, we consider peak current, which has become critical for circuit's reliability in deep submicron design. Traditional low power design techniques focus on the reduction of average power. We propose to reduce peak current while keeping the overhead on average power as small as possible. Second, dual Vt technique and gate sizing have been used simultaneously for leakage savings. However, this approach becomes less effective in deep submicron design. We propose to use the newly developed process-induced mechanical stress to enhance its performance. Finally, in deep submicron design, the impact of on-chip temperature variation on leakage and performance becomes more and more significant. We propose a temperature-aware dual Vt approach to alleviate hot spots and achieve further leakage reduction. We also consider this leakage-temperature dependency in the dynamic voltage scaling approach and discover that a commonly accepted result is incorrect for the current technology. We conduct extensive experiments with popular design benchmarks, using the latest industry CAD tools and design libraries. The results show that our proposed enhancements are promising in power saving and are practical to solve the low power design challenges in deep submicron era

Energy Awareness and Scheduling in Mobile Devices and High End Computing

In the context of the big picture as energy demands rise due to growing economies and growing populations, there will be greater emphasis on sustainable supply, conservation, and efficient usage of this vital resource. Even at a smaller level, the need for minimizing energy consumption continues to be compelling in embedded, mobile, and server systems such as handheld devices, robots, spaceships, laptops, cluster servers, sensors, etc. This is due to the direct impact of constrained energy sources such as battery size and weight, as well as cooling expenses in cluster-based systems to reduce heat dissipation. Energy management therefore plays a paramount role in not only hardware design but also in user-application, middleware and operating system design. At a higher level Datacenters are sprouting everywhere due to the exponential growth of Big Data in every aspect of human life, the buzz word these days is Cloud computing. This dissertation, focuses on techniques, specifically algorithmic ones to scale down energy needs whenever the system performance can be relaxed. We examine the significance and relevance of this research and develop a methodology to study this phenomenon. Specifically, the research will study energy-aware resource reservations algorithms to satisfy both performance needs and energy constraints. Many energy management schemes focus on a single resource that is dedicated to real-time or nonreal-time processing. Unfortunately, in many practical systems the combination of hard and soft real-time periodic tasks, a-periodic real-time tasks, interactive tasks and batch tasks must be supported. Each task may also require access to multiple resources. Therefore, this research will tackle the NP-hard problem of providing timely and simultaneous access to multiple resources by the use of practical abstractions and near optimal heuristics aided by cooperative scheduling. We provide an elegant EAS model which works across the spectrum which uses a run-profile based approach to scheduling. We apply this model to significant applications such as BLAT and Assembly of gene sequences in the Bioinformatics domain. We also provide a simulation for extending this model to cloud computing to answers “what if” scenario questions for consumers and operators of cloud resources to help answers questions of deadlines, single v/s distributed cluster use and impact analysis of energy-index and availability against revenue and ROI

Improving the Efficiency of Energy Harvesting Embedded System

In the past decade, mobile embedded systems, such as cell phones and tablets have infiltrated and dramatically transformed our life. The computation power, storage capacity and data communication speed of mobile devices have increases tremendously, and they have been used for more critical applications with intensive computation/communication. As a result, the battery lifetime becomes increasingly important and tends to be one of the key considerations for the consumers. Researches have been carried out to improve the efficiency of the lithium ion battery, which is a specific member in the more general Electrical Energy Storage (EES) family and is widely used in mobile systems, as well as the efficiency of other electrical energy storage systems such as supercapacitor, lead acid battery, and nickel–hydrogen battery etc. Previous studies show that hybrid electrical energy storage (HEES), which is a mixture of different EES technologies, gives the best performance. On the other hand, the Energy Harvesting (EH) technique has the potential to solve the problem once and for all by providing green and semi-permanent supply of energy to the embedded systems. However, the harvesting power must submit to the uncertainty of the environment and the variation of the weather. A stable and consistent power supply cannot always be guaranteed. The limited lifetime of the EES system and the unstableness of the EH system can be overcome by combining these two together to an energy harvesting embedded system and making them work cooperatively. In an energy harvesting embedded systems, if the harvested power is sufficient for the workload, extra power can be stored in the EES element; if the harvested power is short, the energy stored in the EES bank can be used to support the load demand. How much energy can be stored in the charging phase and how long the EES bank lifetime will be are affected by many factors including the efficiency of the energy harvesting module, the input/output voltage of the DC-DC converters, the status of the EES elements, and the characteristics of the workload. In this thesis, when the harvesting energy is abundant, our goal is to store as much surplus energy as possible in the EES bank under the variation of the harvesting power and the workload power. We investigate the impact of workload scheduling and Dynamic Voltage and Frequency Scaling (DVFS) of the embedded system on the energy efficiency of the EES bank in the charging phase. We propose a fast heuristic algorithm to minimize the energy overhead on the DC-DC converter while satisfying the timing constraints of the embedded workload and maximizing the energy stored in the HEES system. The proposed algorithm improves the efficiency of charging and discharging in an energy harvesting embedded system. On the other hand, when the harvesting rate is low, workload power consumption is supplied by the EES bank. In this case, we try to minimize the energy consumption on the embedded system to extend its EES bank life. In this thesis, we consider the scenario when workload has uncertainties and is running on a heterogeneous multi-core system. The workload variation is represented by the selection of conditional branches which activate or deactivate a set of instructions belonging to a task. We employ both task scheduling and DVFS techniques for energy optimization. Our scheduling algorithm considers the statistical information of the workload to minimize the mean power consumption of the application while satisfying a hard deadline constraint. The proposed DVFS algorithm has pseudo linear complexity and achieves comparable energy reduction as the solutions found by mathematical programming. Due to its capability of slack reclaiming, our DVFS technique is less sensitive to small change in hardware or workload and works more robustly than other techniques without slack reclaiming

Resource Management Algorithms for Computing Hardware Design and Operations: From Circuits to Systems

The complexity of computation hardware has increased at an unprecedented rate for the last few decades. On the computer chip level, we have entered the era of multi/many-core processors made of billions of transistors. With transistor budget of this scale, many functions are integrated into a single chip. As such, chips today consist of many heterogeneous cores with intensive interaction among these cores. On the circuit level, with the end of Dennard scaling, continuously shrinking process technology has imposed a grand challenge on power density. The variation of circuit further exacerbated the problem by consuming a substantial time margin. On the system level, the rise of Warehouse Scale Computers and Data Centers have put resource management into new perspective. The ability of dynamically provision computation resource in these gigantic systems is crucial to their performance. In this thesis, three different resource management algorithms are discussed. The first algorithm assigns adaptivity resource to circuit blocks with a constraint on the overhead. The adaptivity improves resilience of the circuit to variation in a cost-effective way. The second algorithm manages the link bandwidth resource in application specific Networks-on-Chip. Quality-of-Service is guaranteed for time-critical traffic in the algorithm with an emphasis on power. The third algorithm manages the computation resource of the data center with precaution on the ill states of the system. Q-learning is employed to meet the dynamic nature of the system and Linear Temporal Logic is leveraged as a tool to describe temporal constraints. All three algorithms are evaluated by various experiments. The experimental results are compared to several previous work and show the advantage of our methods

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

Energy-Quality-Time Optimized Task Mapping on DVFS-enabled Multicores

International audienceMulticore architectures have great potential for energy-constrained embedded systems, such as energy-harvestingwireless sensor networks. Some embedded applications, especially the real-time ones, can be modeled as imprecise computation tasks. A task is divided into a mandatory subtask that provides a baseline Quality-of-Service (QoS) and an optional subtask that refines the result to increase the QoS. Combining dynamic voltage and frequency scaling, task allocation and task adjustment, we can maximize the system QoS under real-time and energy supply constraints. However, the nonlinear and combinatorial nature of this problem makes it difficult to solve. This work first formulates a mixed-integer non-linear programming problem to concurrently carry out task-to-processor allocation, frequencyto- task assignment and optional task adjustment. We provide a mixed-integer linear programming form of this formulation without performance degradation and we propose a novel decomposition algorithm to provide an optimal solution withreduced computation time compared to state-of-the-art optimal approaches (22.6% in average). We also propose a heuristic version that has negligible computation tim