A Pareto-based Optimizer for Workflow Scheduling in Cloud Computing Environment

A scheduling algorithm in cloud computing environment is in charge of assigning tasks of a workflow tocloud’s virtual machines (vms) so that the workflow completion time is minimized. due to the heterogeneity anddynamicity of vms and diversity of tasks size, workflow scheduling is confronted with a huge permutation space andis known as an np-complete problem; therefore, heuristic algorithms are used to reach an optimal scheduling. while the single-objective optimization i.e., minimizing completion time, proposes the workflow scheduling as a np-complete problem, multi-objective optimization for the scheduling problem is confronted with a more permutation space. inour pre vious work, we considered single-objective optimization (minimizing the workflow completion time) usingparticle swarm optimization (pso) algorithm. the current study aims to present a multi-objective optimizer for conflicting objectives using gray wolves optimizer (gwo) where dependencies exist between workflow tasks. we applied our method to epigenomics (balanced) and montage (imbalanced) workflows and compared our results with those of the spea2 algorithm based on parameters of attention quotient, max extension, and remoteness dispersal.