Design A Mathematical Model for Optimizing Energy Consumption for Data Center

Abstract

The need for hosting services in data centers rises as platforms for IT and cloud services require more processing power and storage capacity. The need for electricity to run IT equipment and maintain cool data centers is greater on these platforms. Making data centers as efficient as feasible without compromising the quality of their energy supply has grown more difficult in recent years. This is a result of the growing number of locations that require data storage. As a result, numerous optimization techniques have been developed that leverage machine learning to increase power efficiency. This paper attempts to categorize and rank the various ways in which machine learning algorithms are applied by researchers to optimize the amount of energy used by data centers. It achieves this by examining the principal studies that have been conducted since and. The purpose of this evaluation is to assist scholars in making well-informed decisions about which methodologies to employ. Machine learning is discussed in terms of its potential to increase data centers' power efficiency. The usage of a neural network and optimization inspired by biology is one potential course of action that this study recommends. In order to set the parameters, this was done.