Energy Efficiency Scheduling using Machine Learning Approach

Abstract

To ensure the efficiency of energy in data centers is very vital objective in modern cloud computing. An immense rate of electrical energy consumed by cloud computing each year which results in a lot of expense in prices. Researchers attempt to develop best possible policies within cloud for the resource management that has several parts like workload stabilization scheduling etc. Machine learning encompasses an important role in these kinds of efforts. In cloud computing, scheduling a job is an essential part for optimizing performance and managing resources. The concentration is on specialized cloud environment and effective scheduling of job in virtual machine resource and server level agreement restrictions. In practical terms, a neural network model is proposed in order to decrease the energy utilization of servers that are in data centers. Result of the proposed model illustrate that the energy utilization is less than that of linear regression models.