Vol. 4 No. 1 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

Optimizing Control Plane Performance for Ultra-Scale EKS Clusters

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  • Babulal Shaik,
  • Srikanth Bandi
Babulal Shaik
Cloud Solutions Architect at Amazon Web Services, USA
Srikanth Bandi
Software Engineer at JP Morgan chase, USA
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Published 26-02-2024

Keywords

  • EKS,
  • control plane
Creative Commons License

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

How to Cite

[1]
Babulal Shaik and Srikanth Bandi, “Optimizing Control Plane Performance for Ultra-Scale EKS Clusters ”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 1, pp. 419–438, Feb. 2024, Accessed: Dec. 28, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/223

Abstract

Maintaining high performance and reliability is crucial to ensuring smooth operations in the realm of large-scale cloud infrastructure. Amazon Elastic Kubernetes Service (EKS), a managed Kubernetes platform, has gained popularity for running containerized applications at scale. However, as organizations grow and handle ultra-scale workloads, the performance of the EKS control plane becomes a critical concern. The control plane, responsible for managing the overall health and coordination of the Kubernetes cluster, can face challenges as the scale increases. Several strategies can be implemented to optimize the performance of the control plane in ultra-scale EKS clusters. First, architecture plays a vital role; choosing the correct configuration for the control plane and worker nodes & ensuring network efficiency is key. Additionally, resource allocation is essential to avoid bottlenecks. This involves careful management of computing, memory, and storage resources to ensure the control plane can handle high demands without slowing down. Monitoring also becomes increasingly important in ultra-scale environments, allowing teams to detect performance issues and make necessary real-time adjustments. Organizations can track control plane metrics such as API server latency, performance, & scheduling delays by leveraging the proper monitoring tools. Best practices are crucial for optimal performance, such as optimizing Kubernetes components like etcd, tuning API server settings, and using horizontal pod autoscaling. Furthermore, balancing efficiency with scalability is a challenge that must be addressed, as performance degradation at any point in the control plane could result in significant operational disruptions. As the cloud-native landscape continues to evolve, understanding the nuances of optimizing EKS control plane performance will be essential for businesses relying on containerized applications and Kubernetes orchestration.

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