International Journal of Soft Computing and Software Engineering (JSCSE)
Cloud computing uses a great amount of heterogeneous resources to deliver countless different services to users of distinctive Quality of Services (QoS) requirements. Numerous diverse tasks need to be carried out to meet the vastly different QoS and budget requirements. Workflow scheduling is therefore critical for the success of large-scale cloud computing. Particle Swarm Optimization (PSO) has been adopted for workflow scheduling in cloud computing, yet most existing works focused on a single objective. This paper proposes a tunable fitness function for the PSO algorithm, based on which a workflow schedule may be selected for minimal cost or minimal makespan (completion time), or any level in between.