Incorporation of weighted linear prediction technique and M/M/1 Queuing Theory for improving energy efficiency of Cloud computing datacenters

Abstract

Cloud computing refers to the services supplied over the internet and the hardware and software that delivers such services. It has the capability to cover a large part of the IT industry and make software even more appealing as a service. It can also reshape how IT hardware is designed and acquired. Nevertheless, datacenters that offer cloud applications consumes a large amount of power which can substantially increase operational costs. As technology is evolving and growing at a rapid pace, more people are dependent on technology and utilizing the cloud. There's a larger demand to extend the platforms needed to improve the development of Cloud computing. As a result, in parallel with this development of infrastructure, there has also been a great deal of attention paid to energy consumption in cloud computing technology. This research will report on incorporating weighted linear prediction technique and M/M/1 Queuing Theory for enhancing the energy efficiency of cloud data centers. The goal is to simulate the effect of various workloads on energy consumption of the cloud system using CloudSim or similar software.