Performance Modeling Based Scheduling and Rescheduling of Parallel Applications on Computational Grids

Abstract As computational grids have become popular and ubiquitous, users have access to large num-ber and different types of geographically distributed grid resources. Many computational gridframeworks are composed of multiple distributed sites with each site consisting of one or morededicated or non-dedicated clusters. Jobs submitted to a grid are handled by a mataschedulerwhich interacts with the local schedulers of the clusters for scheduling jobs to the individualclusters. Computational grids have been found to be powerful research-beds for execution ofvarious kinds of parallel applications. When a parallel application is submitted to a grid, themetascheduler has to choose a set of resources from a cluster for application execution. Toselect the best set of resources for application execution, it is important to determine the per-formance of the application. Accurate performance estimates of an application is essential inassisting a grid meta scheduler to efciently schedule user jobs.Thus models that predict execution times of parallel applications on a set of resources anda search procedure (scheduling strategy) which selects the best set of machines within a clusterfor application execution are of importance for enabling the parallel applications on grids. Forefcient execution of large scientic parallel applications consisting of multiple phases, per-formance models of the individual phases should be obtained. Efcient rescheduling strategiesthat can use the per-phase models to adapt the parallel applications to application and resourcedynamics are necessary for maintaining high performance of the applications on grids. A prac-tical and robust grid computing infrastructure that integrates components related to applicationand resource monitoring, performance modeling, scheduling and rescheduling techniques, ishighly essential for large-scale deployment and high performance of scientic applications ongrid systems and hence for fostering high performance computing.4

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