A Robust Strategic Theory based Load Balancing and Resource Allocation in Cloud Environment

Abstract

An on demand elastic service is cloud computing. At particular time, based on clients requirements, software, information, shared resources and other devices are provided in this. For supporting cost effective usage of computing resources, it is designed using distributed computing and virtualization advances and it enhances resource scalability. Based on requirement, business outcomes can scale up or down its resources. On-demand resource allocation challenges are created by customer demand management. To minimize energy, Dynamic Particle Swarm Optimization (DPSO) model is developed in available research works, where CPU utilization are regulated while operating at maximum frequency. Geographically distributed resources like computers, storage etc. are owned by self interested organizations or agents are available in large-scale computing systems of cloud computing. In their own benefit, this resource allocation algorithm can be manipulated by these agents and severe degradation in performance are produced due to its selfish behaviour and its efficiency is also very poor. To solve this kind of problem, a strategy is designed for developing a resource allocation protocols with load balancer in first phase of this research work. In this agents are forced to follow the rules and tell truth. In heterogeneous distributed systems, to solve load balancing problem, a truthful strategy is designed using this strategic theory. Optimal allocation algorithm based on Improved Elephant Herd Optimization (IEHO) is proposed where a truthful payment scheme is admitted by output function which satisfies voluntary participation. Good performance is exhibited by proposed approach as indicated in experimentation results.