Precise makespan optimization via hybrid genetic algorithm for scientific workflow scheduling problem

Abstract

AbstractTask scheduling in scientific workflows represents an NP-hard problem due to the number of interdependent tasks, data transfers, and the possible execution infrastructure assignments in cloud computing. For this reason, metaheuristics are one of the most widely applied optimisation techniques. Makespan is one of the main objectives in this problem. However, this metric needs to be complemented with a quality measure with respect to the actual execution time in order to avoid incurring more costs than expected by using an over-optimistic approximation. This research applies a new enhanced disk-network-computing evaluation model, that takes into account the communication among the storage devices involved, which plays an important role in actual schedules. The model is implemented in a genetic algorithm and the well-known heuristic HEFT. We propose different hybridisation metaheuristics in conjunction with a new accuracy metric to measure the difference between the makespan approximations and the real one. The new evaluation model is able to improve accuracy with respect to the standard model, and the proposed hybrid methods significantly improve makespan in the case of heterogeneous infrastructures.