动态负载平衡的自适应聚类方法

Hau Yee Sit, E. K. Ho, H. Leong, R. Luk, Lai Kuen Ho
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引用次数: 16

摘要

随着低成本网络和Internet的蓬勃发展,人们对分布式系统的依赖迅速增加,开发有效的任务分配技术成为分布式计算的重要问题之一。在过去的几年中,实际使用的负载平衡算法大多采用每步固定任务数的迁移策略。本文提出了一种任务传输方案,该方案在参与服务器之间自适应地传输任务数,以实现负载均衡。自适应是通过采用距离加权最近邻算法的数据挖掘技术,即聚类来实现的。实验结果表明,与其他几种常用方法相比,本文提出的算法具有最好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An adaptive clustering approach to dynamic load balancing
With the rapidly increasing reliance to distributed systems following the prosperity of low cost networking and the Internet, development of effective techniques for task distribution becomes one of the important issues in distributed computing. During the past few years, most of the load balancing algorithms in practical use employed migration policy with a fixed number of tasks in each step. This paper proposes a task transfer scheme with an adaptive number of tasks transferred between the participating servers for load balancing. The adaptation is achieved by a data mining technique, namely, clustering, via employing the distance-weighted nearest neighborhood algorithm. Experiment results show that our proposed algorithm yields the best performance when compared with several other common approaches.
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