Grid Computing by Dikaiakos M.D.

By Dikaiakos M.D.

This publication constitutes the completely refereed post-proceedings of the second one eu AcrossGrid convention, AxGrids 2004, held in Nicosia, Cyprus in January 2004.

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There is also a set of machines on which nodes can execute (with a different execution cost on each machine) and which need different time to transmit data. A machine can execute only one task at a time, and a task cannot start execution until all data from its parent nodes is available. The scheduling problem is to assign the tasks onto machines so that precedence constraints are respected and the makespan is minimized. For an example, see Figure 1, and parts (a), (b), and (c). Previous work has attempted to characterize the robustness of a schedule; in other words, how robust the schedule would be if variations in the estimates used to build the schedule were to occur at run-time [1,3].

Buyya et al. Nimrod/G: An Architecture for a Resource Management and Scheduling System in a Global Computational Grid,. HPC Asia, 2000. 8. J. Cao et al. Gridflow: Workflow management for grid computing. In 3rd Int. Symposium on Cluster Computing and the Grid, pages 198–205, 2003. 9. A. Chervenak et al. Giggle: A framework for constructing scalable replica location services, Proceedings of Supercomputing 2002 (SC2002), November 2002. 10. K. Czajkowski et al. A resource management architecture for metacom-puting systems.

Section 4 evaluates the performance of the policy and, finally, Section 5 concludes the paper. 2 Preliminaries The model used in this paper to represent an application is the directed acyclic graph (DAG), where nodes (or tasks) represent computation and edges represent communication (data flow) between nodes. The DAG has a single entry node and a single exit node. There is also a set of machines on which nodes can execute (with a different execution cost on each machine) and which need different time to transmit data.

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