Energy Optimization Techniques for Green Computing for Maximizing Environmental Sustainability: A Literature Review

This survey examined ten recent IEEE-published works spanning the core domains of data center energy optimiza-tion for green computing: bio-inspired cloud orchestration [1, 5], deep-learning-driven energy forecasting [2, 3], precision cooling control [6, 9, 10], edge data center power manage-ment [4], AI-workload scheduling [7], and renewable energy integration [8]. Across these domains, the reviewed literature collectively demonstrates that meaningful energy reductions—from 9.2% in precision air conditioning [6] to 45% in integrated cloud frameworks [5]—are attainable through algorithmic progressapplied at multiple infrastructure levels. The highest-impact contributions share a common architectural philosophy: tight coupling of optimization intelligence with orchestration lay-ers, predictive-prescriptive pipeline designs, electrothermal-aware resource management, and co-design with renewable supply chains. The structured gap analysis identified seven cross-cutting deficiencies, clustered into prediction and control gaps (G1, G2), integration gaps (G3, G4), and evaluation and sustain-ability gaps (G5, G6, G7). The most critical are the absence of a closed-loop prediction–control architecture (G1) and the disjoint treatment of cooling and compute optimization (G3). The proposed methodology addresses these gaps through a stacked LSTM coupled with a DQN-based DRL agent whose unified action space simultaneously governs VM scheduling, DVFS setpoints, cooling controls, and renewable dispatch. Closing the identified research gaps will demand sustained interdisciplinary collaboration bridging machine learning, systems engineering, thermal engineering, and environmen-tal sustainability science. Achieving industry sustainability targets requires not a reduction in computing capability but a fundamental elevation in the energy intelligence embedded within computing infrastructure.

1.    B. M. Beena, P. C. Ranga, V. Holimath, S. Sridhar, S. S. Kamble,S. P. Shendre, and M. Y. Priya, “Adaptive energy optimization in cloud computing through containerization,” IEEE Access, vol. 13, pp. 159548–159565, Sep. 2025. 2.   Z. Zhang, W. Huang, W. Li, Y. Zhang, Z. Shao, Z. Lian, and Y. Yang, “Data center energy consumption prediction and optimization based on attentional-LSTM and cutting plane method,” in Proc. 10th Int. Conf. Cyber Security and Information Engineering (ICCSIE), 2025, pp. 594–598. 3.   X. Zhang, D. Niu, X. Liu, B. Deng, and Q. Ai, “Deep learning-based data centre energy efficiency analysis and optimisation research,” in Proc. 7th Int. Conf. Energy Systems and Electrical Power (ICESEP), 2025, pp. 1032–1036. 4.   C. Liu, X. Zhang, W. Gao, W. Peng, and M. Chen, “Energy con-sumption optimization of edge data centers based on load aggregation and computing power migration,” in Proc. 6th Int. Conf. Advanced Electrical and Energy Systems (AEES), 2025, pp. 933–938. 5.   A. Badhan, R. Kaur, P. Arora, and R. Garg, “Energy efficient cloud computing: Strategies for reducing data center power consumption,” in Proc. 3rd Int. Conf. Augmented Intelligence and Sustainable Systems (ICAISS), 2025, pp. 1156–1162. 6.   C. Jiang, R. Shao, and J. Yang, “Energy-saving optimization of preci-sion air conditioning system in data center based on E-WOA algorithm,” in Proc. 8th Int. Conf. Electrical, Mechanical and Computer Engi-neering (ICEMCE), 2024, pp. 26–31. 7.   P. C. Melnatami Krishnaram, “Green AI: Optimizing energy efficiency of workloads for sustainable data centers,” in Proc. IEEE Green Technologies Conference (GreenTech), 2025, pp. 148–152. 8.   H. Ettahri, H. Gualous, J. Sabor, and M. F. Yaden, “Optimization and management of hybrid renewable energy system using PSO algorithm: Data center application,” in Proc. 4th Int. Conf. Innovative Research in Applied Science, Engineering and Technology (IRASET), 2024, pp. 1–6. 9.   Y. Li and J. Cen, “Research on energy-saving and optimization control of data center cooling systems,” in Proc. 7th Int. Conf. Internet of Things, Automation and Artificial Intelligence (IoTAAI), 2025, pp. 524–527. 10.    S. Naganandhini, C. Sundar, G. Venkatesan, and R. Sathya, “Towards energy-efficient data centres: A comprehensive analysis of cooling strategies for maximizing efficiency and sustainability,” in Proc. Int. Conf. Intelligent Computing and Control for Engineering and Business Systems (ICCEBS), 2023, pp. 1–7.