CPU MISER: A Performance-Directed, Run-Time System for Power-Aware Clusters

Abstract-Reducing energy consumption for high end computing can bring various benefits such as, reduce operating costs, increase system reliability, and environment respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cluster with the Dynamic Voltage Frequency Scaling (DVFS) technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task's execution time as a whole. Additionally, Green Service Level Agreement is also considered in this paper. By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time. Models and scheduling heuristics are examined with a simulation study. Test results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.

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“…• Some work [16] builds online performance-driven runtime systems to automatically scale processor supply voltages.…”

Section: B Energy Reduction Via Dvfs Techniquementioning

confidence: 99%

Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS

Wang

Laszewski

Dayal

et al. 2010

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing

187

108

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“…So the above approaches may not work for server environments. The work in [37] [18] proposed execution time models to predict the performance impact due to frequency change. The performance was studied in terms of execution time.…”

Section: Related Workmentioning

confidence: 99%

A Gray-Box Feedback Control Approach for System-Level Peak Power Management

Gong

2010

2010 39th International Conference on Parallel Processing

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“…• Some implementations [20] build an online, performancedriven runtime system to automatically scale processor voltages. The first two methods normally require prior knowledge of applications with aides benchmarking and profiling.…”

Section: A Power-aware Cluster Computingmentioning

confidence: 99%

Power-aware scheduling of virtual machines in DVFS-enabled clusters

Laszewski

Wang

Younge

et al. 2009

2009 IEEE International Conference on Cluster Computing and Workshops

227

136

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Abstract-With the advent of Cloud computing, large-scale virtualized compute and data centers are becoming common in the computing industry. These distributed systems leverage commodity server hardware in mass quantity, similar in theory to many of the fastest Supercomputers in existence today. However these systems can consume a cities worth of power just to run idle, and require equally massive cooling systems to keep the servers within normal operating temperatures. This produces CO2 emissions and significantly contributes to the growing environmental issue of Global Warming. Green computing, a new trend for high-end computing, attempts to alleviate this problem by delivering both high performance and reduced power consumption, effectively maximizing total system efficiency. This paper focuses on scheduling virtual machines in a compute cluster to reduce power consumption via the technique of Dynamic Voltage Frequency Scaling (DVFS). Specifically, we present the design and implementation of an efficient scheduling algorithm to allocate virtual machines in a DVFS-enabled cluster by dynamically scaling the supplied voltages. The algorithm is studied via simulation and implementation in a multi-core cluster. Test results and performance discussion justify the design and implementation of the scheduling algorithm.

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CPU MISER: A Performance-Directed, Run-Time System for Power-Aware Clusters

Cited by 172 publications

References 17 publications

Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS

Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS

A Gray-Box Feedback Control Approach for System-Level Peak Power Management

Power-aware scheduling of virtual machines in DVFS-enabled clusters

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