A Hybrid NSGA-II and Reinforcement Learning Framework for Sustainable and Cost-efficient Cloud Computing

Authors

  • J. Banerjee,G. M. Ansari

Keywords:

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Abstract

Cloud computing has fundamentallytransformed the provisioning and utilization of computational
resources, enabling scalable and on-demand services acrossdiverse domains. Despite these advancements, efficiently

References

Abid, A. Khan, and S. Lee, "A hybrid reinforcement learning-based approach for cloud resource scheduling” Journal of Cloud Computing, vol. 10, no. 1, Art. no. 55, 2021, doi: 10.1186/s13677-021-00239-6.

T. Ahmad, A. Rehman, and M. A. Jan, "Energy-aware resource scheduling using Q-learning in cloud environments," Sustain. Comput. Inform. Syst., vol. 34, Art. no. 100668, 2022, doi: 10.1016/j.suscom.2022.100668.

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Published

2025-08-12

How to Cite

J. Banerjee,G. M. Ansari. (2025). A Hybrid NSGA-II and Reinforcement Learning Framework for Sustainable and Cost-efficient Cloud Computing . Journal of Computational Analysis and Applications (JoCAAA), 34(8), 1–10. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3423

Issue

Vol. 34 No. 8 (2025): JOCAAA

Section

Articles

License

Creative Commons License

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