Gray Wolf Optimisation Based Energy Efficient Green Cloud Computing

Abstract

Scheduling workflows on the cloud is recognised to be an NP-complete problem. But metaheuristic algorithms have been successfully tweaked to deal with this issue in a more efficient manner. Grey wolf optimization (GWO) is a fascinating new metaheuristic approach proposed recently for dealing with continuous optimization issues. Here, we propose the IGWO algorithm as a chaotic-theory-enhanced replacement for the GWO method. The proposed strategy has the potential to prevent the system from settling into a local optimum and speed up the rate at which the GWO converges. Using the CloudSim simulator, we simulate the proposed workflow scheduling system, and the results show that our solution is superior to the alternatives in terms of energy efficiency, cost, and maketime.