基于功率感知集群的任务袋负载均衡

G. Terzopoulos, H. Karatza
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引用次数: 5

摘要

在计算系统中节能是一个非常重要的问题。提交给大型系统的很大一部分工作负载是任务包(BoT)应用程序。每个BoT都是互不通信的独立任务的集合。这类任务用于天文学、数据挖掘和图像处理。已有文献从性能角度对BoT负载均衡进行了研究。本文从能源效率的角度研究BoT负载均衡。我们提出了两种负载平衡机制,并通过在异构集群环境中的模拟进行了评估。bot和高优先级实时任务都提交给集群,以便更准确地表示在大规模系统中提交的工作负载。大量的仿真实验表明,通过应用所提出的负载平衡机制,可以在较小的性能下降的情况下实现显著的节能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bag-of-Tasks Load Balancing on Power-Aware Clusters
Energy preservation in computing systems is very important nowadays. A large percentage of the workload submitted to large-scale systems is bag-of-tasks (BoT) applications. Each BoT is a collection of independent tasks that do not communicate with each other. This type of tasks is used in astronomy, data mining and image processing. BoT load balancing has been studied in the literature from a performance perspective. In this paper, we study BoT load balancing from an energy efficiency perspective. We propose two load balancing mechanisms that are evaluated through simulation in a heterogeneous cluster environment. BoTs and high-priority real-time tasks are both submitted to the cluster, in order to represent more accurately the workload submitted in large-scale systems. Extensive simulation experiments show that by applying the proposed load balancing mechanisms, significant energy savings can be achieved with small performance degradation.
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