International Journal of Electrical and Computer Engineering (IJECE) Vol. 10, No. 2, April 2020, pp. 1524∼1532 ISSN: 2088-8708, DOI: 10.11591/ijece.v10i2.pp1524-1532 ❒ 1524 Power consumption prediction in cloud data center using machine learning Deepika T, Prakash P Department of Computer Science and Engineering Amrita School of Engineering, Coimbatore Amrita Vishwa Vidyapeetham, India Article Info Article history: Received Jun 6, 2019 Revised Oct 17,2019 Accepted Oct 25, 2019 Keywords: Cloud computing Machine Learning Physical Machine Power consumption prediction Virtual Machine ABSTRACT The flourishing development of the cloud computing paradigm provides several ser- vices in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pur- suant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A di- verse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased car- bon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power produc- tion. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process. Copyright c  2020 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Deepika T., Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India. Email: t deepika@cb.students.amrita.edu 1. INTRODUCTION Cloud computing is a technological advancement that furnishing with everything as a service such as storage space to the user, networking, server as well as applications. Infrastructure as a Service(IaaS), Software as a Service(SaaS), and Platform as a Service(PaaS) are the different types of service models, in Cloud computing that can be delivered on demand. Cloud providers offer a pool of virtualized computa- tional resources to customers in the data center, in a pay-as-you-go manner [1]. The virtualized computing services provide IaaS that helps reduce the installation and maintenance cost for computing environments. A cloud data center is associated with a group of connected physical machines (PM) or host used by the organizations for network processing, remote storage and access to enormous data. The data centers are the backbone for the cloud environments. The exponential growth of cloud computing, because of emerging tech- nologies like IIOT (Industrial Internet of Things) applications, big data evolution, and 5G functionality. In 2020, Journal homepage: http://ijece.iaescore.com/index.php/IJECE