Scheduling in Heterogeneous Grid Environments: The Effects of Data Migration

Abstract

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources.However, a number of major technical hurdles must be overcome before this goal can be fully realized.One problem critical to the effective utilization of computational grids is efficient job scheduling.Our prior work addressed this challenge by defining a grid scheduling architecture and several job migration strategies.The focus of this study is to explore the impact of data migration under a variety of demanding grid conditions.We evaluate our grid scheduling algorithms by simulating compute servers, various groupings of servers into sites, and inter-server networks, using real workloads obtained from leading supercomputing centers.Several key performance metrics are used to compare the behavior of our algorithms against reference local and centralized scheduling schemes.Results show the tremendous benefits of grid scheduling, even in the presence of input/output data migration -while highlighting the importance of utilizing communication-aware scheduling schemes.