Real-time power management in virtualized data centers using predictive analytics

Abstract

With the growing demand for cloud services and the proliferation of data-intensive applications, power management in virtualized data centers has become increasingly critical. Efficient power management not only reduces operational costs but also minimizes the environmental impact of data center operations. This paper explores the implementation of real-time power management in virtualized data centers using predictive analytics. By leveraging machine learning algorithms to predict power consumption patterns, we can optimize resource allocation, enhance energy efficiency, and maintain performance. This study outlines a framework for integrating predictive analytics into power management systems, evaluates its potential benefits, and discusses the challenges involved.