A Bio-Inspired and Heuristic-Based Hybrid Algorithm for Effective Performance With Load Balancing in Cloud Environment

Abstract

In a cloud computing environment, effective scheduling policies and load balancing have always been the aim. An efficient task scheduler must be proficient in a dynamically distributed environment and to the policy of efficient scheduling of jobs based upon the workload. In this research, a novel hybrid heuristic algorithm is developed for balancing the load among cloud nodes. This is achieved by hybridizing the existing ant colony optimization (ACO), artificial bee colony algorithm (ABC), and AHP (analytical hierarchy process) algorithm. The AHP algorithm and the artificial bee colony (ABC) algorithm is used for figuring out the best servers suitable for a particular job, and the ant colony algorithm is used to find the most efficient path to that particular server. The proposed algorithm is better in resource utilization. It also performs better load balancing, which keeps on improving with time. The result analysis shows better average response time and better average makespan time compared to other two existing algorithms.