Enhancing Cloud Energy Efficiency with Machine Learning and AI Techniques

DOI

10.2991/978-94-6239-768-2_8How to use a DOI?

Keywords

Cloud Computing; resource management; energy consumption; cloud data centres; virtual machines; machine learning and AI-Based Optimization

Abstract

The energy optimization of cloud environments poses a crucial challenge due to the rising demand for cloud computing services and environmental concerns. Promising tools toward improving the energy efficiency in cloud data centers are ML and AI. This paper explores a number of ML and AI-based approaches for optimizing energy consumption in cloud infrastructures. They can include energy-efficient resource provisioning, dynamic scaling, predictive maintenance, smart job scheduling, or optimized cooling. AI/ML models will predict and correct power consumption anomalies and, in turn, balance out workloads to avoid significant waste of energy. To add to the energy-related footprint reduction, energy-sensitive algorithms can minimize energy usage along with certain edge computing strategies. This paper uniquely introduces the combination of these methods within an integrated framework for cloud data centers. End. Overall, AI and ML can contribute substantially to achieving greener cloud computing environments by improving operational efficiency and reducing energy costs.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

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TY  - CONF
AU  - Ashish Semwal
AU  - Manmohan Singh Rauthan
AU  - Varun Barthwal
AU  - Ashish Agarwal
AU  - Ashish Joshi
PY  - 2026
DA  - 2026/09/07
TI  - Enhancing Cloud Energy Efficiency with Machine Learning and AI Techniques
BT  - Proceedings of the Third International Conference on Recent Advances in Computing Sciences (RACS 2025)
PB  - Atlantis Press
SP  - 67
EP  - 76
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-768-2_8
DO  - 10.2991/978-94-6239-768-2_8
ID  - Semwal2026
ER  -