A Particle Swarm Optimization Approach for Workflow Scheduling on Cloud Resources Priced by CPU Frequency

In this paper, we propose a procedure based on Particle Swarm Optimization (PSO) to guide the user in splitting an amount of CPU capacity (sum of frequencies) among a fixed number of resources in order to minimize the execution time (makespan) of the workflow. The proposed procedure was evaluated and compared with a naive approach, which selects only identical CPU frequency configurations for resources. Simulation results show that, by keeping the overall amount of provisioned CPU frequency constant, the proposed PSO-based approach was able to reduce the makespan of the workflow by carefully selecting different CPU frequencies for resources.