Local Minima Jump PSO for Workflow Scheduling in Cloud Computing Environments

摘要

Earlier Grids implemented workflows, but the former's reduced performance has resulted in workflows being implemented in cloud. Cloud computing the latest in distributed computing, facilitates virtualized resources for applications. Cloud computing environment workflows enable use of varied cloud services facilitating workflow execution. Good workflow examples are online banking, insurance claim processing, e-business, and e-government scenarios. As workflow scheduling is NP hard, meta-heuristic based methods solve issues. This paper attempts to locate a suitable workflow schedule where Particle Swarm Optimization (PSO) is used to optimize load balancing, speedup ratio, and makespan. Experimental results demonstrate the effectiveness of the proposed algorithm. The proposed algorithm is more effective with higher number of tasks.