Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing

Abstract

Cloud computing is a technology that enables the delivery of various computing services over the Internet. The Resource Scheduling (RS) and Load Balancing (LB) mechanisms are essential for the cloud to provide consistent results. The submitted tasks by the users are computed on the cloud platform using its Virtual Machines (VMs). The cloud ensures an ideal LB mechanism, where no VMs will be overloaded or idle. This research paper focuses on this LB mechanism by experimenting in the WorkflowSim environment and computing tasks using the Sipht task dataset. The RS algorithms First Come First Serve (FCFS), Maximum – Minimum (Max – Min), Minimum Completion Time (MCT), Minimum – Minimum (Min – Min), and Round-Robin (RR) are utilized to balance the computational load of VMs. The experiment was conducted in four phases, where the Sipht task dataset varied in task length in each phase. Each phase included sixteen scenarios, where each scenario differed from another by the number of VMs used. The final results of this experiment convey that the load balanced by the algorithms FCFS, Max – Min, MCT, Min – Min, and RR were 51.98 %, 41.71 %, 51.98 %, 59.43 %, and 52.17 %, respectively, across all four phases. Lastly, the Reinforcement Learning (RL) model is suggested to add an intelligence mechanism to LB and optimize the cloud resource utilization using these RS algorithms to provide the best Quality of Service (QoS).