An Intelligent Optimization Model for Energy-Efficient Cloud Computing Systems
DOI:
https://doi.org/10.66280/cset.v1i1.92Keywords:
Energy-Efficient Cloud Computing, Intelligent Optimization, Sustainable Infrastructure, Resource Orchestration, Green AI, Systems Governance, Hyperscale Data Centers.Abstract
The exponential growth of cloud-based services has precipitated an unprecedented surge in the energy consumption of hyperscale data centers, positioning environmental sustainability as a primary constraint in systems engineering. This paper presents an extensive analytical investigation into an intelligent optimization model designed to enhance energy efficiency within large-scale cloud computing environments. We move beyond simplistic power-saving heuristics to examine a holistic socio-technical framework that integrates machine learning-driven resource orchestration with institutional governance and physical infrastructure management. The research scrutinizes the structural trade-offs between computational performance, service-level reliability, and energy parsimony, arguing that true efficiency is achieved only when algorithmic intelligence is coupled with robust deployment strategies. We explore the deployment of dynamic voltage and frequency scaling, virtual machine consolidation, and carbon-aware workload scheduling, contextualizing these techniques within the broader requirements of thermal management and hardware longevity. Furthermore, the paper addresses the policy implications of automated energy management, the ethical imperatives of fairness in resource distribution among heterogeneous users, and the necessity of transparent auditing for corporate sustainability reporting. By synthesizing perspectives from distributed systems, artificial intelligence, and environmental policy, this work provides a comprehensive roadmap for the next generation of "Green Cloud" architectures. We conclude that the transition toward energy-efficient cloud systems requires a paradigm shift from peak-performance optimization to a steady-state equilibrium that prioritizes long-term ecological and operational viability.
References
1.Abadie, A. (2021). Using machine learning for industrial energy estimation and prediction. Journal of Economic Literature, 59(2), 606-640.
2.Armbrust, M., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
3.Barham, P., et al. (2003). Xen and the art of virtualization. ACM SIGOPS Operating Systems Review, 37(5), 164-177.
4.Beloglazov, A., et al. (2012). Energy-aware resource allocation heuristics for efficient management of cloud computing data centers. Future Generation Computer Systems, 28(5), 755-768.
5.Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
6.Buyya, R., et al. (2010). Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308.
7.Chen, Y., et al. (2019). Energy-efficient resource management in cloud computing: A survey. Journal of Systems and Software, 151, 1-22.
8.Dayarathna, M., et al. (2016). Data center energy consumption modeling: A survey. IEEE Communications Surveys & Tutorials, 18(1), 732-794.
9.Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.
10.Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for time-series predictions. European Journal of Operational Research, 270(2), 654-669.
11.Ghodsi, A., et al. (2011). Dominant Resource Fairness: Fair allocation of multiple resource types. NSDI '11: 8th USENIX Conference on Networked Systems Design and Implementation.
12.Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
13.Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
14.Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in Neural Information Processing Systems.
15.He, K., et al. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
16.Hellerstein, J. L., et al. (2004). Feedback Control of Computing Systems. John Wiley & Sons.
17.Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
18.Hull, J. C. (2021). Machine Learning in Business: An Introduction to the World of Data Science. Pearson.
19.Jennings, B., & Stadler, R. (2014). Resource management in clouds: Survey and research challenges. Journal of Network and Systems Management, 23(3), 567-619.
20.Katz, R. H. (2009). The information technology infrastructure for the 21st century. Communications of the ACM, 52(4), 11-13.
21.Koomey, J. (2011). Growth in data center electricity use 2005 to 2010. Analytics Press.
22.LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
23.Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A, 379(2194), 20200209.
24.Manvi, S. S., & Shyam, G. K. (2014). Resource management with a focus on virtualization in cloud computing: A survey. Journal of Network and Computer Applications, 38, 1-16.
25.Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. NIST Special Publication, 800-145.
26.Newman, M. E. J. (2010). Networks: An Introduction. Oxford University Press.
27.Paszke, A., et al. (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems.
28.Rossi, G. (2018). Socio-Technical Systems and the Finance Industry. Routledge.
29.Schwartz, R., et al. (2020). Green AI. Communications of the ACM, 63(12), 54-63.
30.Shiller, R. J. (2015). Irrational Exuberance. Princeton University Press.
31.Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.
32.Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
33.Zaharia, M., et al. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. NSDI '12: 9th USENIX Conference on Networked Systems Design and Implementation.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Computer Science and Engineering Transactions
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
