Opposition learning-based grey wolf optimizer algorithm for parallel machine scheduling in cloud environment

Abstract

Cloud computing is a novel developing computing paradigm where implementations, information, and IT services are given over the internet. The parallel-machine scheduling (Task-Resource) is the important role in cloud computing environment. But parallel-machine scheduling issues are premier that associated with the efficacy of the whole cloud computing facilities. A good scheduling algorithm has to decrease the implementation time and cost along with QoS necessities of the consumers. To overcome the issues present in the parallel-machine scheduling, we have proposed an oppositional learning based grey wolf optimizer (OGWO) on the basis of the proposed cost and time model on cloud computing environment. Additionally, the concept of opposition based learning is used with the standard GWO to enhance its computational speed and convergence profile of the proposed method. The experimental results show that the proposed method outperforms among all methods and provides quality schedules with less memory utilization and computation time.