City Research Online

Abstract

In this paper we address the problem of balancing the processing load of MapReduce tasks running on heterogeneous clusters, i.e., clusters composed of nodes with different capacities and update cycles. We present a fully decentralized algorithm, based on ratio consensus, where each mapper decides the amount of workload data to handle for a single user job using only job specific local information, i.e., information that can be collected from directly connected neighboring mappers, regarding their current workload usage and capacity. In contrast to other algorithms in the literature, the proposed algorithm can be deployed in heterogeneous clusters and can operate asynchronously in both directed and undirected communication topologies. The performance of the proposed algorithm is demonstrated via simulation experiments on large-scale strongly connected topologies.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Computer Science

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