AI-Driven Kubernetes Orchestration: Utilizing Intelligent Agents for Automated Cluster Management and Optimization
DOI:
https://doi.org/10.48047/brsw0z42Keywords:
Kubernetes, AI-driven orchestration, machine learning, container orchestration, automated cluster management, resource optimization, predictive analytics, reinforcement learning, workload scheduling, fault tolerance, cloud computing, DevOps, Site Reliability Engineering (SRE), anomaly detection, multi-cloud management, scalability, self-healing systems, intelligent automation, cloud-native applications, cost optimization.Abstract
With Kubernetes, container orchestration became more efficient and faster due to efficient deployment and scaling of applications. Yet, traditional Kubernetes management still must often be tuned via manual configurations or static configurations, which are less efficient. This paper presents a survey for AI based approaches on Kubernetes orchestration including intelligent agent, machine learning based techniques and automated optimization. It proposes a comparative evaluation in the forms like performance metrics i.e., resource utilization, scalability, fault tolerance and operational cost reduction between traditional and AI enhanced Kubernetes
management..
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References
J. Smith, “AI-enhanced Kubernetes orchestration: A survey,” IEEE Transactions on Cloud Computing, vol. 18, no. 2, pp. 233–245, 2022.
M. Brown and K. Patel, “Machine learning in Kubernetes scaling,” Journal of Cloud Computing, vol. 9, no. 1, pp. 1–15, 2021.
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