Dynamic Multi-objective task scheduling in Cloud Computing based on Modified particle swarm optimization

Abstract

Task scheduling is one of the most important research topics in Cloud Computing environment. Dynamic Multi-objective task scheduling in Cloud Computing are proposed by using modified particle swarm optimization. This paper presents efficient allocation of tasks to available virtual machine in user level base on different parameters such as reliability, time, cost and load balancing of virtual machine. Agent used to create dynamic system. We propose mathematical model multi-objective Load Balancing Mutation particle swarm optimization (MLBMPSO) to schedule and allocate tasks to resource. MLBMPSO considers two objective functions to minimize round trip time and total cost. Reliability can be achieved in system by getting task failure to allocate and reschedule with available resource based on load of virtual machine. Experimental results demonstrated that MLBMPSO outperformed the other algorithms in time and cost.