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Efficient scheduling of task graph collections on heterogeneous resources

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3 Author(s)

In this paper, we focus on scheduling jobs on computing grids. In our model, a grid job is made of a large collection of input data sets, which must all be processed by the same task graph or workflow, thus resulting in a collection of task graphs problem. We are looking for a competitive scheduling algorithm not requiring complex control. We thus only consider single-allocation strategies. In addition to a mixed linear programming approach to find an optimal allocation, we present different heuristic schemes. Then, using simulations, we compare the performance of our different heuristics to the performance of a classical scheduling policy in Grids, HEFT. The results show that some of our static-scheduling policies take advantage of their platform and application knowledge and outperform HEFT, especially under communication-intensive scenarios. In particular, one of our heuristics, DELEGATE, almost always achieves the best performance while having lower running times than HEFT.

Published in:

Parallel & Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on

Date of Conference:

23-29 May 2009

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