Efficient Scheduling of Task Graph Collections on Heterogeneous Resources

Matthieu Gallet (1, 2) , Loris Marchal (1, 2) , Frédéric Vivien (1, 2)

1 LIP - Laboratoire de l'Informatique du Parallélisme
2 GRAAL - Algorithms and Scheduling for Distributed Heterogeneous Platforms

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.

Matthieu Gallet, Loris Marchal, Frédéric Vivien. Efficient Scheduling of Task Graph Collections on Heterogeneous Resources. IPDPS'2009 - International Parallel and Distributed Processing Symposium, 2009, Roma, Italy. ⟨10.1109/IPDPS.2009.5161045⟩. ⟨hal-00786257⟩