IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An ant colony optimization algorithm for the time-varying workflow scheduling problem in grids
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A knowledge-based ant colony optimization for a grid workflow scheduling problem
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Stochastic DAG scheduling using a Monte Carlo approach
Journal of Parallel and Distributed Computing
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Scheduling is an important factor for the efficient execution of computational workflows on Grid environments. A large number of static scheduling heuristics has been presented in the literature. These algorithms allocate tasks before job execution starts and assume a precise knowledge of timing information, which may be difficult to obtain in general. To overcome this limitation of static strategies, dynamic scheduling strategies may be needed for a changing environment such as the Grid. While they incur runtime overheads, they may better adapt to timing changes during job execution. In this work, we analyse five well-known heuristics (min-min, max-min, sufferage, HEFT and random) when used as static and dynamic scheduling strategies in a grid environment in which computing resources exhibit congruent performance differences. The analysis shows that nonlist based heuristics are more sensitive than list-based heuristics to inaccuracies in timing information. Static list-based heuristics perform well in the presence of low or moderate inaccuracies. Dynamic versions of these heuristics may be needed only in environments where high inaccuracies are observed. Our analysis also shows that list-based heuristics significantly outperform non-list based heuristics in all cases and, therefore, constitute the most suitable strategies by which to schedule workflows either statically or dynamically.