H. Siegel, Bhavesh Khemka, Ryan D. Friese, S. Pasricha, A. A. Maciejewski, G. Koenig, Sarah Powers, Marcia Hilton, Jendra Rambharos, Gene Okonski, Steve Poole
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引用次数: 4
Abstract
This corresponds to the material in the invited keynote presentation by H. J. Siegel, summarizing the research in [1], [2]. We address the problem of assigning dynamically-arriving tasks to machines in a heterogeneous computing environment. These machines execute a workload composed of different tasks, where the tasks have diverse computational requirements. Each task has a utility function associated with it that represents the value of completing that task, and this utility decreases the longer it takes a task to complete. The goal of our resource manager is to maximize the sum of the utilities earned by all tasks arriving in the system over a given interval of time, while satisfying an energy constraint. We describe example energy-aware resource management methods to accomplish this goal, and compare their performance. We also study the bi-objective problem of maximizing system utility and minimizing the system energy consumption. This analysis technique allows system administrators to investigate the trade-offs between these conflicting goals.
这与H. J. Siegel邀请的主题演讲中的材料相对应,总结了[1],[2]中的研究。我们解决了在异构计算环境中给机器分配动态到达任务的问题。这些机器执行由不同任务组成的工作负载,其中任务具有不同的计算需求。每个任务都有一个与之关联的实用函数,它表示完成该任务的值,该实用函数的值随着任务完成所需时间的延长而减少。我们的资源管理器的目标是在满足能量约束的情况下,最大化在给定时间间隔内到达系统的所有任务所获得的实用程序的总和。我们描述了实现这一目标的示例能源感知资源管理方法,并比较了它们的性能。研究了系统效用最大化和系统能耗最小化的双目标问题。这种分析技术允许系统管理员调查这些相互冲突的目标之间的权衡。