A Discrete Interval-Based Multi-Objective Memetic Algorithm for Scheduling Workflow With Uncertainty in Cloud Environment

Abstract

In consideration of the uncertainty of the scientific workflows, an interval-based multi-objective cloud workflow scheduling problem is investigated, which widely exists in the cloud environment. This problem aims at allocating the workflow to the public cloud environment. The uncertain workload and communication data of workflow as well as the processing ability and bandwidth of the resources are represented by an interval number, which models the uncertainty of these variables. The objectives are to minimize the total execution time and cost. To address this problem, a discrete interval-based multi-objective memetic algorithm (DIMOMA) is proposed. A hybrid initial strategy is employed to generate the potential population. With the contribution-based selection mechanism, the self-adaptive genetic operators are designed to perform a global search in the problem space. Then, a novel local search procedure is incorporated to perform intensification and accelerate the convergence. A comprehensive computational experiment and comparisons with several meta-heuristics adapted from the related problems are conducted based on an extended benchmark set. The simulated results reveal that the proposed method can achieve better trade-off fronts between the execution time and cost of workflow. On the performance metric hypervolume which measures both execution time and cost, the proposed DIMOMA can improve by 3.90%, 9.30%, 6.25%, and 7.74% compared with EMS-C, MOACS, ch-PICEA-g, and I_MaOPSO, respectively. Besides, DIMOMA can achieve better robustness, which means the difference between the lower and upper bounds of the execution time and cost of the solutions obtained by DIMOMA are over smaller than the state-of-the-art algorithms.