Bi-objective Hybrid Particle Swarm Optimization & Ant Colony Optimization Workflow Scheduling Algorithm for Cloud
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Abstract
Abstract: Cloud Computing is the latest distributed computing paradigm. The recent increased use of workflow management systems by large scientific collaborations presents the challenge of scheduling large-scale workflows onto distributed resources. As workflow scheduling belongs to the NP-complete problem, So meta-heuristic approaches are a better option. But most of the existing studies try to optimize only one of the objectives. But the need of the hour is to focus on multiple-objective like time, cost, CPU utilization, Reliability and energy optimization etc. In this paper, our focus is on two objectives, makespan and cost, to be optimized simultaneously using two meta-heuristic search techniques PSO and ACO for scheduling workflow. To solve this bi-objective Time & Cost optimization workflow scheduling problem, we present, a hybrid of particle swarm optimization with cost function optimize using ant colony optimization. For the initialization of task to resources, we are using Pareto distribution(PD), a normal-like distribution. Simulation result shows that hybridization of PSO and ACO performs better than the existing BPSO(HEFT+PSO) Technique.
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