Energy Optimal Activation of Processors for the Execution of a Single Task with Unknown Size

Jonatha Anselmi, B. Gaujal
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引用次数: 1

Abstract

A key objective in the management of modern computer systems consists in minimizing the electrical energy consumed by processing resources while satisfying certain target performance criteria. In this paper, we consider the execution of a single task with unknown size on top of a service system that offers a limited number of processing speeds, say $N$, and investigate the problem of finding a speed profile that minimizes the resulting energy consumption subject to a deadline constraint. Existing works mainly investigated this problem when speed profiles are continuous functions. In contrast, the novelty of our work is to consider discontinuous speed profiles, i.e., a case that arises naturally when the underlying computational platform offers a finite number of speeds. In our main result, we show that the computation of an optimal speed profile boils down to solving a convex optimization problem. Under mild assumptions, for such convex optimization we prove some structural results that yield the formulation of an extremely efficient solution algorithm. Specifically, we show that the optimal speed profile can be computed by solving $O(\log N)$ one-dimensional equations. Our results hold when the task size follows a known probability distribution function and the set of available speeds, if listed in increasing order, forms a sublinear concave sequence.
执行未知大小单任务的处理器能量最优激活
现代计算机系统管理的一个关键目标是在满足某些目标性能标准的同时,尽量减少处理资源所消耗的电能。在本文中,我们考虑在提供有限数量的处理速度(例如$N$)的服务系统上执行一个未知大小的单个任务,并研究在最后期限约束下找到最小化最终能耗的速度配置文件的问题。现有的工作主要是研究速度曲线为连续函数时的这一问题。相比之下,我们工作的新颖之处在于考虑了不连续的速度曲线,即当底层计算平台提供有限数量的速度时自然出现的情况。在我们的主要结果中,我们证明了最优速度剖面的计算归结为解决一个凸优化问题。在温和的假设下,对于这种凸优化,我们证明了一些结构结果,这些结果产生了一个非常有效的求解算法的公式。具体来说,我们证明了最优速度分布可以通过求解$O(\log N)$一维方程来计算。当任务大小遵循已知的概率分布函数和可用速度集时,我们的结果成立,如果按递增顺序列出,则形成次线性凹序列。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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