Integration of Scheduling and Replication in Data Grids

Abstract

AbstractData Grids seek to harness geographically distributed resources for large-scale data-intensive problems. Such problems involve loosely coupled jobs and large data sets distributed remotely. Data Grids have found applications in scientific research fields of high-energy physics, life sciences etc. as well as in the enterprises. The issues that need to be considered in the Data Grid research area include resource management for computation and data. Computation management comprises scheduling of jobs, scalability, and response time; while data management includes replication and movement of data at selected sites. As jobs are data intensive, data management issues often become integral to the problems of scheduling and effective resource management in the Data Grids. The paper deals with the problem of integrating the scheduling and replication strategies. As part of the solution, we have proposed an Integrated Replication and Scheduling Strategy (IRS) which aims at an iterative improvement of the performance based on the coupling between the scheduling and replication strategies. Results suggest that, in the context of our experiments, IRS performs better than several well-known replication strategies.KeywordsSchedule StrategyData GridQueue SizeReplication StrategyData ReplicationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.