A Particle Grey Wolf Hybrid Algorithm for Workflow Scheduling in Cloud Computing

A workflow consists of a set of tasks that are dependent on each other and scheduling these dependent tasks to the virtual machines is one of the complex problems in cloud computing. Moreover, workflow scheduling becomes more complex with the increasing number of tasks and virtual machines and considered to be an NP-hard problem. Therefore, the meta-heuristic approaches have been used to find out optimal scheduling of workflow schedules. The proposed algorithm named PSO-GWO is the combination of two well-known meta-heuristic algorithms Particle Swarm Optimization and Grey Wolf Optimization. The experiment result shows that the PSO-GWO algorithm decreases the average total execution cost and average total execution time in comparison to standard Particle Swarm Optimization and Grey Wolf Optimization algorithm.