Improved Genetic Algorithm with Min-Min Approach for Efficient Task Scheduling in Cloud Computing

Abstract

Cloud computing has emerged to be a significant and prevalent computing model, which helps to provide services on demand. It renders metered services. Making efficient use of resources through the reduction of execution time and expense and consequently, maximizing the profit is the primary objective of cloud service provider. Hence, making use of efficient scheduling algorithms still remains an important challenge in cloud computing. Job planning as well as weight balancing in the Virtual Machine (VM) and reducing the makes pan involved in completing the tasks are the important research worries. In this work, Improved Genetic Algorithm with Min-Min (IGAMM) approach is proposed for efficient task scheduling over cloud. In the newly introduced technique, the workload imposed on machines gets added and reduced down as per their power. The major aim of this technique is to reduce the make span time, increase the usage of resources, and decrease the amount of energy consumed. A technique is introduced for workload scheduling in accordance with grouping of VMs in cloud environments. The objective of the novel technique is to improve the performance of cloud computing by minimizing the make span and response time, and by maximizing the usage of VM. The comparison of the novel algorithm is carried out with the available techniques in terms of different performance metrics. The results of the evaluation reveal that the performance IGAMM algorithm proposed is much superior compared to the existing techniques.