Task Scheduling based on Modified Grey Wolf Optimizer in Cloud Computing Environment

Abstract

Task scheduling is considered as one of the most critical problems in cloud computing environment. The main target of task scheduling includes scheduling jobs on virtual machines as well as improves performance. This study employed Grey Wolf Optimization (GWO) algorithm with modifications on the fitness function by making it handles multi-objectives in single fitness; the makespan and cost are the objectives included in the fitness in order to solve task scheduling problem. The main target of this technique is to reduce both cost and makespan. CloudSim tool is used to evaluate the objectives of the proposed method. The simulation results showed that the proposed method (Modified Grey Wolf Optimizer - MGWO) has better performance than both the traditional Grey Wolf Optimization Algorithm (GWO) and Whale Optimization Algorithm (WOA) with makespan based fitness in terms of makespan, cost and degree of imbalance.