Performance Evaluation of Meta-Heuristic Techniques for Workflow Scheduling in Heterogeneous Cloud System
Cloud computing is one of the hottest topics of research in the current era of technology that is used to provide hardware and software services on a paid basis. Many meta-heuristic techniques are useful to improve the performance of a cloud in the form of workflow scheduling or by other means. In this research, Ant Colony Optimization, Particle Swarm Optimization, and Cat Swarm Optimization are compared for workflow scheduling. Workflows are scientific tasks. After simulation, it is identified that Cat Swarm Optimization performs better than Ant Colony Optimization and Particle Swarm Optimization whereas Particle Swarm Optimization works better than Ant Colony Optimization.
Keywords - Ant Colony Optimization (ACO), Cloud Computing, Cat Swarm Optimization (CSO), Particle Swarm Optimization (PSO), Virtual Machines (VMs)