Resource selection and allocation for dynamic adaptive computing in heterogeneous clusters

Abstract

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.

Process Rescheduling in High Performance Computing Environments

“…In this context, we developed a new heuristic named AutoMig that selects one or more candidates for migration automatically. We took advantage of both List Scheduling (Duselis et al, 2009) and Backtracking (Baritompa et al, 2007) concepts to evaluate the migration impact on each element of the PM list in an autonomous fashion. In addition, other AutoMig's strength comprises the needlessness to complete an additional MigBSP parameter for getting more than one migratable process on rescheduling activation.…”