Evolutionary Algorithms in Cloud Computing from the Perspective of Energy Consumption: A Review

Abstract

Cloud Computing provides utility-based IT services. The services are available as pay per use. Cloud gives advantage to organizations in setting up fundamental hardware and software requirements i.e. instead of purchasing hardware or software cloud services can be used. The availability of cloud services any time and anywhere makes it a feasible solution for many applications. cloud services are constrained by some parameters such as Quality of Service (QoS), efficient utilization of cloud resources, user budget, user deadlines, energy consumption etc. In this article, we present a comprehensive review of techniques or algorithms designed to reduce energy consumption in cloud data centers. The review covers Evolutionary Algorithms (EA) such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Genetic Algorithms (GA). We discuss each technique with strengths and weaknesses. Target objectives of each algorithm are also compared. The article is concluded with future research directions.