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Research Paper | Computer Science and Information Technology | Volume 11 Issue 2, February 2022 | Pages: 1358 - 1361 | India
Optimizing Resource Management in Kubernetes Clusters with Reinforcement Learning
Abstract: With the increasing complexity of cloud-native applications, optimizing resource management in Kubernetes clusters has become a critical challenge. This paper investigates the use of Reinforcement Learning (RL) to optimize resource allocation in Kubernetes clusters, specifically deployed on Amazon Web Services (AWS). The approach integrates RL algorithms with Kubernetes to dynamically adjust resource allocation based on real-time workloads, balancing performance and cost efficiency. Through comprehensive experiments conducted on AWS, this research demonstrates significant improvements in resource utilization and cost savings. The findings provide valuable insights into intelligent resource management strategies in cloud computing environments.
Keywords: Kubernetes Resource Management, Reinforcement Learning, Deep Q-Network, Cluster Autoscaling, Container Orchestration
How to Cite?: Ayisha Tabbassum, Shaik Abdul Kareem, "Optimizing Resource Management in Kubernetes Clusters with Reinforcement Learning", Volume 11 Issue 2, February 2022, International Journal of Science and Research (IJSR), Pages: 1358-1361, https://www.ijsr.net/getabstract.php?paperid=SR22210212814, DOI: https://dx.doi.org/10.21275/SR22210212814