DE-GWO: A Multi-objective Workflow Scheduling Algorithm for Heterogeneous Fog-Cloud Environment

Abstract

Abstract The demand for a quick response from cloud services is rapidly increasing day-by-day. Fog computing is a trending solution to fulfil the demands. When integrated with the cloud, this technology can tremendously improve the performance. Like any other technology, Fog also has the shortcoming of limited resources. Efficient scheduling of tasks among limited resources is one of the significant issues for research. This paper proposes a multi-objective hybrid task scheduling algorithm named Differential evolution-Grey wolf optimization (DE-GWO), which combines Differential evolution (DE) and Grey wolf optimization (GWO) approach to address the workflow scheduling issue. The proposed algorithm is applied on five different scientific workflows (Montage, Epigenomics, SIPHT, LIGO and Cybershake) and evaluated on three performance indicators (execution time, energy consumption and cost). The DE method is chosen as the evolutionary pattern of wolves to speed up convergence and enhance GWO's accuracy. Simulation results show that the DE-GWO performs better than the other traditional and recently proposed optimization algorithms, since DE incorporates evolution and elimination mechanisms in GWO and GWO retains a good balance between exploration and exploitation.