Power consumption prediction in cloud data center using machine learning

Abstract

The flourishing development of the cloud computing paradigm provides several services 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. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon 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 production. 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.

Hybrid Load Balancing Approach based on the Integration of QoS and Power Consumption in Cloud Computing

“…As such, allowing servers to run with a limited workload is not cost effective. In the context of cloud computing, a major problem for service providers is the amount of power used by cloud data centers [14], [18]. In this regard, the goal is to decrease power use and ensure compliance with environmental policies and standard inter-user contracts [14].…”

Enhancing fault tolerance: dual Q-learning with dynamic scheduling

“…Singh et al [23] present a hardware setup for vehicle-to-vehicle communication under foggy conditions. Deepika and Prakash [24] focus on power consumption prediction in cloud data centers using machine learning techniques. Sandeep and Thangam [25] propose a hybrid cloud approach for efficient data storage and security.…”

Prediction of Power Consumption Utilization in a Cloud Computing Data Centre using Kalman Filter parameters with Genetic Algorithm

“…Also, in Nigeria, total amount of $4b is believed to be spent annually in the last three years by four big players (MTN, 9Mobile, Glo and Airtel) in the telecommunication industry and also expected to grow further in the coming years because of an increase in data demand [12]. Therefore, reduction in energy cost by a small percentage will result in a huge amount of savings in the operation cost of data centres [13,14]. This has more effect in a country like Nigeria, where there is inadequacy in utility grid power systems supply to support the emerging trend.…”