Resource allocation using democratic Grey Wolf optimization in cloud computing environment

Abstract

Cloud computing is one of the growing environment that outlines an internet-based computing system consisting of a large number of computers and other devices, where a number of things, such as computer infrastructure, access to applications, software, processing power, etc. are shared over the Internet. The allocation of the cloud resources to the user based on their request is a Non-deterministic Polynomial-time (NP) issue, which consumes more time. Therefore, heuristic methods utilize for optimizing resource allocation. In this research work, the Democratic Grey Wolf Optimization (DGWO) is proposed to overcome the limitations of the resource allocation in efficient way. In this method, DGWO performs the following steps; initializing the request size, generating requests, and estimate fitness value of DGWO, sorting, dividing and evaluating the requests of the user. The advantages of DGWO are higher speed convergence, easier implementation, global optimization capacity. The DGWO has high performance in unknown, challenging search spaces which has high local optima avoidance. The efficiency of DGWO is improved in terms of searching optimum resource time where the searching behavior is obtained from GWO. The DGWO method achieved 75% throughput, 96% allocation of resources compared to other meta-heuristic existing techniques.