Resource selection and allocation for dynamic adaptive computing in heterogeneous clusters

• Main Authors: Duselis, J.U., Cauich, E.E., Wang, R.K., Scherson, I.D.
• Format: Conference Proceeding
• Language: English
• Subjects: Application software; Clustering algorithms; Computer applications; Computer science; Concurrent computing; Current measurement; Pervasive computing; Power engineering computing; Power system modeling; Resource management
• ISSN: 1552-5244; 2168-9253
• DOI: 10.1109/CLUSTR.2009.5289204

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.