Optimal scheduling workflows in cloud computing environment using Pareto-based Grey Wolf Optimizer

A workflow consists of dependent tasks, and scheduling of a workflow in a cloud environment means the arrangement of tasks of the workflow on virtual machines (VMs) of the cloud. By increasing VMs and the diversity of task size, we have a huge number of such arrangements. Finding an arrangement with minimum completion time among all of the arrangements is an Non-Polynomial-hard problem. Moreover, the problem becomes more complex when a scheduling should consider a couple of conflicting objectives. Therefore, the heuristic algorithms have been paid attention to figure out an optimal scheduling. This means that although the single-objective optimization, ie, minimizing completion time, proposes the workflow scheduling as an NP-complete problem, multiobjective optimization for the scheduling problem is confronted with a more permutation space because an optimal trade-off between the conflicting objectives is needed. To this end, we extended a recent heuristic algorithm called Grey Wolf Optimizer (GWO) and considered dependency graph of workflow tasks. Our experiment was carried out using the WorkflowSim simulator, and the results were compared with those of 2 other heuristic task scheduling algorithms.