A Strategy of Cloud Resource Load Balancing Enhancement Based on Ant Colony Optimization

Abstract

Cloud computing plays a core role in the era of big data and artificial intelligence. In cloud environment, the unbalanced resource allocation of server cluster greatly affects the performance of the system. To address this challenge, in this paper we proposed a load balancing strategy for cloud resource scheduling based on ant colony algorithm. By redefining pheromone and heuristic information used in classical ant colony algorithm, server load factor is introduced to provide dynamic changes of the server load to achieve the optimal balanced scheduling of the whole cluster. Three main parameters including CPU, memory, and network bandwidth utilization are considered in our strategy. Through a serials of experiments conducted on CloudSim simulation platform, our proposed strategy shows better performance compared to classical ant colony algorithm and load balancing ant colony algorithm in terms of task completion time, CPU utilization, and load balancing degree.