Optimizing Energy Efficiency in Public Sector Cloud Data Centers Through Machine Learning Driven Steganographic Manner

The next generation of data centers is evolving with rapid pace, and the cloud computing infrastructures focus more and more on saving power. This has led to heightened interest in novel ways to optimize energy usage. Out of several methods aimed at minimizing energy expenditure while also enabling a sustainable approach, Virtual Machine (VM) consolidation stands out prominently. Cutting edge computational intelligence methods like and especially machine learning and deep learning have shown very good results for resource allocation optimization in cloud data centers. However, traditional strategies for VM migration mainly center on total energy usage and tend to ignore the energy costs of migration, such as latency, data transfer overhead, and distance. To fill in this void, we performed an exhaustive review of over 40 research works analyzing different energy-efficient techniques, such as Support Vector Machines (SVM), Decision Trees, Deep Learning, and Reinforcement Learning (RL). Our focus rests primarily on RL based approaches, and how effectively they reduce energy expenditure in relation to the resources being utilized. This analysis aims to identify the best performing RL methods that can improve the energy efficiency of cloud computing systems.