Vol. 3 No. 1 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

Utilizing Machine Learning for Optimizing Kubernetes Scheduler Performance

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  • Babulal Shaik,
  • Jayaram Immaneni,
  • Srikanth Bandi
Babulal Shaik
Cloud Solutions Architect at Amazon Web Services, USA
Jayaram Immaneni
SRE Lead at JP Morgan Chase, USA
Srikanth Bandi
Software Engineer at JP Morgan chase, USA
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Published 22-06-2023

Keywords

  • Machine learning,
  • Kubernetes scheduler
Creative Commons License

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

How to Cite

[1]
Babulal Shaik, Jayaram Immaneni, and Srikanth Bandi, “Utilizing Machine Learning for Optimizing Kubernetes Scheduler Performance ”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 1, pp. 469–489, Jun. 2023, Accessed: Dec. 28, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/222

Abstract

Kubernetes, a widely adopted open-source platform for managing containerized applications, plays a crucial role in automating tasks like deployment, scaling, and orchestration of workloads across a cluster of machines. At the heart of Kubernetes is the scheduler, which determines how and where to place workloads, or pods, on the available nodes within the cluster. The efficiency of this scheduling process is vital for maintaining system performance, especially as Kubernetes clusters grow in size and complexity. While the default scheduler in Kubernetes is functional, it often faces challenges when dealing with large-scale or dynamic workloads that require real-time resource management. This is where machine learning (ML) comes into play. By integrating ML techniques, Kubernetes schedulers can be enhanced to predict resource usage more accurately, optimize pod placement, and make more intelligent scheduling decisions. ML models can analyze past usage patterns, anticipate the resource requirements of incoming workloads, and adjust scheduling strategies accordingly. This approach can significantly improve performance, reduce resource contention, and ensure better load balancing, all contributing to a more efficient and reliable system. However, incorporating ML into the Kubernetes scheduler is challenging. The integration must be seamless with existing scheduling algorithms and should not compromise the stability or predictability of the system. There are also concerns about the overhead introduced by ML models and the need for constant retraining to ensure they adapt to evolving workloads. Nevertheless, the potential benefits of ML-enhanced Kubernetes scheduling are substantial, including improved scalability, responsiveness, and resource efficiency. As Kubernetes continues to grow, leveraging ML for more innovative scheduling promises to be a key factor in optimizing the performance of large-scale cloud-native environments.

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