AI-Driven Resource Management in Cloud Computing : A Review

Authors

  • Vijay Ramamoorthi  Independent Researcher, USA

DOI:

https://doi.org/10.32628/IJSRST24113260

Keywords:

Cloud Computing, Artificial Intelligence, Resource Management, Multi-Tenant Optimization, Dynamic Resource Allocation, Energy Efficiency, Reinforcement Learning.

Abstract

Cloud computing has revolutionized the delivery of computational resources by providing scalable and elastic infrastructure. However, the increasing complexity of cloud environments presents significant challenges in resource management, including dynamic allocation, energy efficiency, and multi-tenant optimization. Traditional methods often fail to meet the demands of modern cloud systems, leading to inefficiencies, high operational costs, and compromised Quality of Service (QoS). This paper reviews the transformative role of Artificial Intelligence (AI) in addressing these challenges. AI-driven techniques, such as machine learning, reinforcement learning, and optimization algorithms, enable predictive analytics, adaptive scaling, and efficient workload distribution. Key applications include dynamic resource allocation, energy optimization, and intelligent scheduling in multi-tenant systems. By synthesizing current advancements and identifying challenges, this study highlights the potential of AI to enhance cloud computing efficiency, scalability, and sustainability. The findings provide a roadmap for researchers and practitioners to develop next-generation cloud systems powered by AI.

References

  1. W. Dawoud, I. Takouna, and C. Meinel, “Scalability and performance management of Internet applications in the cloud,” in Advances in Systems Analysis, Software Engineering, and High Performance Computing, IGI Global, 2013, pp. 434–464.
  2. N. F. Mir, “AI-driven management of dynamic multi-tenant cloud networks,” in SoutheastCon 2023, Orlando, FL, USA, 2023, pp. 716–717.
  3. N. Mungoli, “Scalable, distributed AI frameworks: Leveraging cloud computing for enhanced deep learning performance and efficiency,” arXiv [cs.LG], 26-Apr-2023.
  4. D.-M. Bui, Y. Yoon, E.-N. Huh, S. Jun, and S. Lee, “Energy efficiency for cloud computing system based on predictive optimization,” J. Parallel Distrib. Comput., vol. 102, pp. 103–114, Apr. 2017.
  5. L. Margatama, “Reducing energy consumption in green cloud computing,” Helix, vol. 11, no. 2, pp. 6–15, May 2021.
  6. M. A. Khoshkholghi, M. N. Derahman, A. Abdullah, S. Subramaniam, and M. Othman, “Energy-efficient algorithms for dynamic virtual machine consolidation in cloud data centers,” IEEE Access, vol. 5, pp. 10709–10722, 2017.
  7. M. Gaggero and L. Caviglione, “Predictive control for energy-aware consolidation in cloud datacenters,” IEEE Trans. Control Syst. Technol., pp. 1–1, 2015.
  8. S. Zhu, K. Ota, and M. Dong, “Energy-efficient artificial intelligence of things with intelligent edge,” IEEE Internet Things J., vol. 9, no. 10, pp. 7525–7532, May 2022.
  9. A. Osman, A. Sagahyroon, R. Aburukba, and F. Aloul, “Optimization of energy consumption in cloud computing datacenters,” Int. J. Electr. Comput. Eng. (IJECE), vol. 11, no. 1, p. 686, Feb. 2021.
  10. Y. Nan et al., “Adaptive energy-aware computation offloading for cloud of things systems,” IEEE Access, vol. 5, pp. 23947–23957, 2017.
  11. J. OuYang, C. Ding, and L. Dai, “The optimization of Energy for Cloud Computing,” Open Autom. Control Syst. J., vol. 6, no. 1, pp. 1742–1747, Dec. 2014.
  12. B. P. Rimal and M. Maier, “Workflow scheduling in multi-tenant cloud computing environments,” IEEE Trans. Parallel Distrib. Syst., vol. 28, no. 1, pp. 290–304, Jan. 2017.
  13. Y. Wang, Q. He, X. Zhang, D. Ye, and Y. Yang, “Efficient QoS-aware service recommendation for multi-tenant service-based systems in cloud,” IEEE Trans. Serv. Comput., pp. 1–1, 2017.
  14. N. F. Mir, “AI-assisted edge computing for multi-tenant management of edge devices in 6G networks,” in 2023 2nd International Conference on 6G Networking (6GNet), Paris, France, 2023.
  15. Orchestrating Efficiency: AI-Driven Cloud Resource Optimization for Enhanced Performance and Cost Reduction. .
  16. G. Peng, H. Wang, J. Dong, and H. Zhang, “Knowledge-based resource allocation for collaborative simulation development in a multi-tenant cloud computing environment,” IEEE Trans. Serv. Comput., vol. 11, no. 2, pp. 306–317, Mar. 2018.
  17. Efficient QoS-Aware Service Recommendation for Multi-Tenant Service-Based Systems in Cloud. .
  18. T. C. Chieu, A. Mohindra, and A. A. Karve, “Scalability and performance of web applications in a compute cloud,” in 2011 IEEE 8th International Conference on e-Business Engineering, Beijing, China, 2011.
  19. R. Yang and J. Xu, “Computing at massive scale: Scalability and dependability challenges,” in 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE), Oxford, United Kingdom, 2016.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2024-02-29

Issue

Volume 11, Issue 1, January-February-2024

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

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

How to Cite

[1]
Vijay Ramamoorthi "AI-Driven Resource Management in Cloud Computing : A Review" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 11, Issue 1, pp.683-696, January-February-2024. Available at doi : https://doi.org/10.32628/IJSRST24113260