Dynamic mapping of a class of independent tasks onto heterogeneous computing systems
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
When the Herd Is Smart: Aggregate Behavior in the Selection of Job Request
IEEE Transactions on Parallel and Distributed Systems
Metrics and Benchmarking for Parallel Job Scheduling
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Benchmarks and Standards for the Evaluation of Parallel Job Schedulers
IPPS/SPDP '99/JSSPP '99 Proceedings of the Job Scheduling Strategies for Parallel Processing
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
Adaptive hierarchical scheduling policy for enterprise grid computing systems
Journal of Network and Computer Applications
Replica-Aware job scheduling in distributed systems
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
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In the Data Grid environment, the primary goal of data replication is to shorten the data access time that is experienced by the job and reduce the job turnaround time as a consequence. After introducing a Data Grid architecture that supports efficient data access for the Grid job, two dynamic data replication algorithms are put forward. Combined with different Grid scheduling heuristics, the performances of the data replication algorithms are evaluated with various simulations. The simulation results demonstrate that the dynamic replication algorithms can reduce the job turnaround time remarkably. Especially the combination of Shortest Turnaround Time (STT) scheduling heuristic and Centralized Dynamic Replication (CDR) algorithm exhibits prominent performance in diverse conditions of workload and system environment.