Crow Search based Scheduling Algorithm for Load Balancing in Cloud Environment

Abstract

Task scheduling is a vital aspect in computer science, as it is the essence of how a computer executes various tasks and performs the related activities with accuracy and efficiency. In cloud computing, task scheduling is a typical NP-hard problem and scientists have been attempting to handle this problem for quite a long time. Although it is anything but difficult to accomplish a global optimum solution utilizing the ant colony algorithm and achieve promising outcomes. This research paper proposes a crow search based load balancing algorithm (CSLBA) for multi-objective task scheduling environment which concentrates on allocating best suitable resources for the task to be implemented with the consideration of various parameters like average makespan time (AMT), average waiting time (AWT) and average data center processing time (ADCPT). The present work provides a comparative analysis of proposed algorithm and Ant Colony Optimization based load balancing algorithm (ACOLBA). The experimentation is performed in steps and comparative analysis proved that proposed CSLBA is the optimal technique among the other scheduling technique considered in this research paper.