Stanford Database Library of American Journal of Applied Science and Technology

Vol. 6 No. 03 (2026)
Articles

Carbon-Aware Multi-Objective Scheduling and Optimization in Geo-Distributed Cloud and Machine Learning Systems: A Sustainable Computing Paradigm

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  • Maria Chokwuk
Maria Chokwuk
Department of Computer Science, University of Edinburgh, United Kingdom

Published 2026-03-31

Keywords

  • Carbon-aware computing,
  • multi-objective scheduling,
  • cloud computing,
  • machine learning sustainability

How to Cite

Maria Chokwuk. (2026). Carbon-Aware Multi-Objective Scheduling and Optimization in Geo-Distributed Cloud and Machine Learning Systems: A Sustainable Computing Paradigm. Stanford Database Library of American Journal of Applied Science and Technology, 6(03), 125–129. Retrieved from http://oscarpubhouse.com/index.php/sdlajast/article/view/1346

Copyright (c) 2026 Maria Chokwuk

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The rapid proliferation of artificial intelligence (AI), cloud computing, and large-scale machine learning (ML) systems has significantly increased global computational demand, raising urgent concerns about energy consumption and carbon emissions. While recent advancements in model architectures and hardware acceleration have improved computational efficiency, the environmental footprint of these technologies remains substantial. This study investigates the integration of carbon-aware strategies into scheduling and optimization frameworks for geographically distributed cloud environments. Drawing upon contemporary research in carbon measurement, federated learning, workload migration, and evolutionary optimization, this work proposes a comprehensive theoretical model for multi-objective scheduling that simultaneously optimizes performance, cost, and carbon emissions. The study critically examines existing approaches such as carbon-aware federated learning, virtual machine placement, and checkpointing mechanisms, and contextualizes them within broader scheduling paradigms including heuristic, metaheuristic, and evolutionary algorithms. Furthermore, the research highlights the role of real-time carbon monitoring systems and geo-distributed infrastructure in enabling dynamic decision-making. The findings suggest that integrating carbon-awareness into scheduling algorithms can significantly reduce emissions without compromising computational performance. However, challenges such as data heterogeneity, system latency, and trade-off management persist. This paper contributes to the growing body of sustainable computing literature by offering an in-depth theoretical synthesis and proposing future research directions for scalable, environmentally responsible computing systems.

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References

  1. Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023).
  2. Gupta, U., Kim, Y.G., Lee, S., Tse, J., Lee, H.-H.S., Wei, G.-Y., Brooks, D., Wu, C.-J. Chasing carbon: the elusive environmental footprint of computing. IEEE International Symposium on High-Performance Computer Architecture (2021), pp. 854–867.
  3. Patterson, D., Gonzalez, J., Hölzle, U., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D.R., Texier, M., Dean, J. The carbon footprint of machine learning training will plateau, then shrink. Computer, 55(7) (2022), pp. 18–28.
  4. Dodge, J., Prewitt, T., Tachet des Combes, R., Odmark, E., Schwartz, R., Strubell, E., Luccioni, A.S., Smith, N.A., DeCario, N., Buchanan, W. Measuring the carbon intensity of AI in cloud instances. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (2022), pp. 1877–1894.
  5. Zhu, B., Deng, Z., Song, X., Zhao, W., Huo, D., Sun, T., Ke, P., Cui, D., Lu, C., Zhong, H., et al. Carbonmonitor-power near-real-time monitoring of global power generation on hourly to daily scales. Scientific Data, 10(1) (2023), p. 217.
  6. Bian, J., Wang, L., Ren, S., Xu, J. CAFE: carbon-aware federated learning in geographically distributed data centers. Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems (2024), pp. 347–360.
  7. Xu, W., Huang, X., Meng, S., Zhang, W., Guo, L., Sato, K. An efficient checkpointing system for large machine learning model training. SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis (2024), pp. 896–900.
  8. Park, J., Kim, D., Kim, J., Han, J., Chun, S. Carbon-aware and fault-tolerant migration of deep learning workloads in the geo-distributed cloud. IEEE International Conference on Cloud Computing (2024), pp. 494–501.
  9. Khodayarseresht, E., Shameli-Sendi, A., Fournier, Q., Dagenais, M. Energy and carbon-aware initial VM placement in geographically distributed cloud data centers. Sustainable Computing: Informatics and Systems, 39 (2023), Article 100888.
  10. Mishra, M.K., Patel, Y.S., Rout, Y., Mund, G.B. A survey on scheduling heuristics in grid computing environment. International Journal of Modern Education and Computer Science, pp. 57–83 (2014).
  11. Tsai, C., Rodrigues, J. Meta heuristic scheduling for cloud: A survey. IEEE Systems Journal, 8(1), 279–291 (2014).
  12. Zhou, Z., Hu, Z. Task scheduling algorithm based on greedy strategy in cloud computing. Open Cybernetics and Systemics Journal, 8, 111–114 (2014).
  13. Kong, X., Lin, C., Jiang, Y., Yan, W., Chu, X. Efficient dynamic task scheduling in virtualized data centers with fuzzy prediction. Journal of Network and Computer Applications, 34(4), 1068–1077 (2011).
  14. Sun, W., et al. A game theoretic resource allocation model based on extended second price sealed auction in grid computing. Journal of Computers, 7(1), 65–75 (2012).
  15. Grover, R., Chabbra, A. Bio-inspired optimization techniques for job scheduling in grid computing. IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (2016), pp. 1902–1906.
  16. Bagchi, T.P. The nondominated sorting genetic algorithm: NSGA. In Multiobjective Scheduling by Genetic Algorithms. Springer (1999).
  17. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197 (2002).
  18. Coello Coello, C.A., Lechuga, M.S. MOPSO: a proposal for multiple objective particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation (2002), pp. 1051–1056.
  19. Li, H., Landa-Silva, D. An adaptive evolutionary multi-objective approach based on simulated annealing. Evolutionary Computation, pp. 561–595 (2011).
  20. Lopez-Ibanez, M., Stutzle, T. The automatic design of multiobjective ant colony optimization algorithms. IEEE Transactions on Evolutionary Computation, 16(6), 861–875 (2012).
  21. Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z. Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1), 32–49 (2011).
  22. Yang, S., et al. A grid-based evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 17(5), 721–736 (2013).
  23. S. Bhat, S. R. Sirikonda, V. Katoch and R. Jain, "Carbon-Kube: A Kubernetes-Native Framework for Multi-Objective Carbon-Aware Scheduling of Big Data Pipelines," 2026 9th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech), Kolkata, India, 2026, pp. 1-6, doi: 10.1109/IEMENTech202669403.2026.11434192.