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 novel 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.