A Review of PSO-Based Task and Workflow Scheduling Analysis in Cloud Environment

Abstract

Cloud computing has become one of the fastest emerging technologies of this era. In cloud computing one of the main challenges is efficient allocation of resources. Efficient resource allocation in cloud computing environments boils down to task and workflow scheduling methods. In literature several methods have been employed for cloud resource allocation. One of the promising methods of cloud resource allocation is Particle Swarm Optimization (PSO) which is an established technique in generalized optimization problems. Though PSO related algorithms have been used for cloud computing models for the last one decade, a detailed comparison of existing PSO based algorithms for task and workflow scheduling is not readily available. In this context, this paper presents a comparison of different PSO based algorithms for task and workflow scheduling in cloud computing environments. Based on our comparison it is observed that each PSO based algorithm has its own advantages and disadvantages for cloud resource allocation.