Main Article Content
The rapid proliferation of cloud computing has transformed the way businesses and organizations deploy and manage their applications. Ensuring optimal performance of these applications in the cloud environment is critical for achieving high levels of user satisfaction and operational efficiency. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing cloud application performance by leveraging data-driven insights and automation. This paper explores the various ways in which AI is being used to improve cloud application performance, including resource allocation, predictive scaling, anomaly detection, and intelligent load balancing. Through an in-depth analysis of these techniques, this paper highlights the benefits, challenges, and future prospects of incorporating AI into cloud application performance optimization.
- 1. Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing National Institute of Standards and Technology, Special Publication, 800 (145), 7.
- 2. Marzolla, M., & Mirandola, R. (2019). A survey on self-adaptive software systems in cloud computing IEEE Transactions on Cloud Computing, 7(2), 372–386,
- 3. Chen, Y., Paxson, V., & Katz, R. H. (2002). What's new about cloud computing security? University of California, Berkeley, USA, 1–16.
- 4. Youssef, M., & Kosta, S. (2017). Machine learning in the cloud: Evaluating the current landscape and future prospects IEEE Cloud Computing, 4(4), 24-33.
- 5. Wood, T., Shenoy, P., Venkataramani, A., Yousif, M., & Ramakrishnan, K. K. (2012). Sandpiper: Black-box and gray-box resource management for virtual machines ACM Transactions on Computer Systems (TOCS), 30(4), 1-38
- 6. Choi, H. S., & Jin, H. (2018). Predictive auto-scaling for containerized cloud applications using LSTM neural networks Future Generation Computer Systems, 78, 450–462.
- 7. Zohrevand, S., & Buyya, R. (2020). Deep reinforcement learning for autonomous cloud management: A survey Journal of Parallel and Distributed Computing, 137, 58–75.
- 8. Gholami, S., Hu, J., Raahemi, B., & Hsu, H. H. (2018). Anomaly detection in cloud data centers: A review ACM Computing Surveys (CSUR), 51(6), 1-36.
- 9. Shahzad, F., Shahid, M., Islam, S. U., & Zhang, G. (2019). A survey of artificial intelligence techniques in load balancing IEEE Access, 7, 118152–118172.
- 10. Korupolu, M. R., Menon, A. G., & Singh, A. (2008). Dynamic power allocation to servers in internet data centers In Proceedings of the 2008 ACM/IEEE Conference on Supercomputing (pp. 1–12).
- 11. Narayanan, D., & Buyya, R. (2021). A survey on container orchestration and management systems ACM Computing Surveys (CSUR), 54(4), 1-36.
- 12. Alharthi, A. A., Hassan, S. U., Rehman, M. H. U., & Alzahrani, A. L. (2020). Performance analysis and comparison of machine learning algorithms for cloud computing Future Generation Computer Systems, 104, 195-209.
- 13. Ahuja, R., Wang, W., Gupta, I., & Butt, A. R. (2021). A survey of AI techniques for optimizing cloud computing Journal of Cloud Computing: Advances, Systems, and Applications, 10(1), 1–30
- 14. Xiong, L., & Perros, H. (2019). Artificial intelligence-based solutions for the cloud computing stack: A survey Journal of Network and Computer Applications, 125, 1–25.
- 15. Zia, T., & Li, J. (2017). Reinforcement learning-based resource allocation and management in cloud computing: A survey Journal of Network and Computer Applications, 95, 32–49.
- 16. Jiang, H., & Buyya, R. (2017). Virtual machine provisioning based on analytical performance and QoS in cloud computing environments Journal of Network and Computer Applications, 85, 1–13.
- 17. Fazeli, M., & Zomaya, A. Y. (2017). A survey of autonomous cloud computing Journal of Parallel and Distributed Computing, 108, 61–84.