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Resource selection and allocation for dynamic adaptive computing in heterogeneous clusters

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4 Author(s)
John U. Duselis ; The Donald Bren School of Information and Computer Science, University of California, Irvine, Irvine, CA 92697 ; E. Enrique Cauich ; Richert K. Wang ; Isaac D. Scherson

This paper provides a framework for dynamic adaptive computing in heterogeneous clusters for computationally intensive applications. The framework considers a set of discoverable interconnected computational resources and either a parallel or sequential workload needing to be executed. An adaptive inclusion/exclusion algorithm is used to select the resources by using novel performance measurements and profiling techniques. Furthermore, contrary to a greedy approach where all the resources are seized for the workload application, our framework only harnesses the best fit resources measured against system-wide performance characterization, and is contingent upon the current workload definition. The intelligent selection of a subset of resources has proven to achieve better performance; especially in environments with a high level of heterogeneity where the characteristics of some resources may not achieve the best performance the cluster can provide. Additionally, this paper provides a novel analysis of the workload and cluster characteristics, exhibiting analytical starting points to be used in the resource selection.

Published in:

2009 IEEE International Conference on Cluster Computing and Workshops

Date of Conference:

Aug. 31 2009-Sept. 4 2009