AI-Driven Infrastructure Automation: Leveraging AI and ML for Self-Healing and Auto-Scaling Cloud Environments

Authors

  • Ali Asghar Mehdi Syed Senior DevOps Engineer, InfraOps at Imprivata, USA. Author
  • Erik Anazagasty Sr. Devops Engineer at Imprivata, USA. Author

DOI:

https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P104

Keywords:

AI-driven automation, machine learning, cloud computing, self-healing, auto-scaling, cloud infrastructure, predictive analytics, DevOps, AIOps, Kubernetes, cloud security, anomaly detection, elasticity, fault tolerance, adaptive scaling

Abstract

By allowing self-healing & the auto-scaling features that improve dependability, efficiency & the cost-effectiveness, AI-driven infrastructure automation is changing the cloud environments. Conventional manual management approaches are unable to handle the unanticipated workloads, security concerns & the operational failures as cloud systems have developed in the complexity. By automating reactions to system failures, resource fluctuations & the performance constraints, artificial intelligence (AI) & machine learning (ML) have become indispensable for improving the cloud operations. Self-healing systems employ artificial intelligence (AI) to detect the anomalies, predict problems & independently carry out corrective actions such resource reallocation, vulnerability correction or rebuilt failing services. On the other hand, auto-scaling guarantees best performance by changing computing capacity in line with actual time demand, therefore lowering expenses. Predictive analytics, reinforcement learning & the artificial intelligence-driven monitoring systems that constantly evaluate system behavior & the distribute resources appropriately define advanced automation methods. Including artificial intelligence into cloud infrastructure management helps companies to reduce downtime, improve security, and maximize operational performance. Driven by artificial intelligence, automation improves cloud operations by eliminating the need for human capacity planning and troubleshooting, therefore enabling faster firm development. As artificial intelligence develops, self-optimizing, autonomous systems competent of actual time adaptability to changing the conditions will define cloud infrastructure. By enabling the creation of more durable, scalable & the reasonably priced cloud infrastructures, this change lets companies focus on the growth & the innovation instead of IT complexities

References

[1] Sekar, Jeyasri, and L. L. C. Aquilanz. "Autonomous cloud management using AI: Techniques for self-healing and self-optimization." Journal of Emerging Technologies and Innovative Research 11 (2023): 571-580.

[2] Dash Karan, Mark Steven. "AI-Driven Cloud Computing: Enhancing Scalability, Security, and Efficiency." (2022).

[3] Vankayalapati, Ravi Kumar, and Chandrashekar Pandugula. "AI-Powered Self-Healing Cloud Infrastructures: A Paradigm For Autonomous Fault Recovery." Migration Letters 19.6 (2022): 1173-1187.

[4] Sarvari, Peiman A., et al. "Next-Generation Infrastructure and Application Scaling: Enhancing Resilience and Optimizing Resource Consumption." Global Joint Conference on Industrial Engineering and Its Application Areas. Cham: Springer Nature Switzerland, 2023.

[5] De Vleeschauwer, Danny, et al. "5Growth data-driven AI-based scaling." 2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit). IEEE, 2021.

[6] Papagianni, Chrysa, et al. "5Growth: AI-driven 5G for Automation in Vertical Industries." 2020 European Conference on Networks and Communications (EuCNC). IEEE, 2020.

[7] Benzaid, Chafika, and Tarik Taleb. "AI-driven zero touch network and service management in 5G and beyond: Challenges and research directions." Ieee Network 34.2 (2020): 186-194.

[8] Ganesan, Premkumar. "Advancing Application Development through Containerization: Enhancing Automation, Scalability, and Consistency." North American Journal of Engineering Research 2.3 (2021).

[9] Friesen, Maxim, Lukasz Wisniewski, and Jürgen Jasperneite. "Machine learning for zero-touch management in heterogeneous industrial networks-a review." 2022 IEEE 18th International Conference on Factory Communication Systems (WFCS). IEEE, 2022.

[10] Malikireddy, Sai Kiran Reddy. "Transforming SME cloud cost management with artificial intelligence." International Journal of Cloud Computing and Services Science 9.3 (2020): 112-124.

[11] Liyanage, Madhusanka, et al. "A survey on zero touch network and service management (ZSM) for 5G and beyond networks." Journal of Network and Computer Applications 203 (2022): 103362.

[12] Asimiyu, Zainab. "Optimizing Healthcare System Operations with Kubernetes: A Comprehensive Guide." (2021).

[13] Vankayalapati, Ravi Kumar. "AI Clusters and Elastic Capacity Management: Designing Systems for Diverse Computational Demands." Available at SSRN 5115889 (2022).

[14] Aisyah, Nur. "Quantitative Analysis of Distributed Denial-of-Service Mitigation Approaches in Global E-Commerce Cloud Operations." Perspectives on Next-Generation Cloud Computing Infrastructure and Design Frameworks 5.10 (2021): 1-8.

[15] Liyanagea, Madhusanka, et al. "A Survey on Zero Touch Network and Service (ZSM) Management for 5G and Beyond Networks." English, Journal of Network and Computer Applications 4 (2022): 103.

Published

2024-03-26

Issue

Section

Articles

How to Cite

1.
Mehdi Syed AA, Anazagasty E. AI-Driven Infrastructure Automation: Leveraging AI and ML for Self-Healing and Auto-Scaling Cloud Environments. IJAIDSML [Internet]. 2024 Mar. 26 [cited 2025 Oct. 2];5(1):32-43. Available from: https://ijaidsml.org/index.php/ijaidsml/article/view/81