Self-Adaptive PSO Memetic Algorithm For Multi Objective Workflow Scheduling in Hybrid Cloud

Cloud computing is a technology in distributed computing that facilitate pay per model to solve large scale problems. The main aim of cloud computing is to give optimal access among the distributed resources. Task scheduling in cloud is the allocation of best resource to the demand considering the different parameters like time, makespan, cost, throughput etc. All the workflow scheduling algorithms available cannot be applied in cloud since they fail to integrate the elasticity and heterogeneity in cloud. In this paper, the cloud workflow scheduling problem is modeled considering make span, cost, percentage of private cloud utilization and violation of deadline as four main objectives. Hybrid approach of Particle Swarm Optimization (PSO) and Memetic Algorithm (M4) called Self-Adaptive Particle Swarm Memetic Algorithm (SPMA) is proposed. SPMA can be used by cloud providers to maximize user quality of service and the profit of resource using an entropy optimization model. The heuristic is tested on several workflows. The results obtained shows that SPMA performs better than other state of art algorithms.