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On the Optimization of Kubernetes toward the Enhancement of Cloud Computing

Author

Listed:
  • Subrota Kumar Mondal

    (School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China
    These authors contributed equally to this work.)

  • Zhen Zheng

    (School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China
    These authors contributed equally to this work.)

  • Yuning Cheng

    (School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China)

Abstract

With the vigorous development of big data and cloud computing, containers are becoming the main platform for running applications due to their flexible and lightweight features. Using a container cluster management system can more effectively manage multiocean containers on multiple machine nodes, and Kubernetes has become a leader in container cluster management systems, with its powerful container orchestration capabilities. However, the current default Kubernetes components and settings have appeared to have a performance bottleneck and are not adaptable to complex usage environments. In particular, the issues are data distribution latency, inefficient cluster backup and restore leading to poor disaster recovery, poor rolling update leading to downtime, inefficiency in load balancing and handling requests, poor autoscaling and scheduling strategy leading to quality of service (QoS) violations and insufficient resource usage, and many others. Aiming at the insufficient performance of the default Kubernetes platform, this paper focuses on reducing the data distribution latency, improving the cluster backup and restore strategies toward better disaster recovery, optimizing zero-downtime rolling updates, incorporating better strategies for load balancing and handling requests, optimizing autoscaling, introducing better scheduling strategy, and so on. At the same time, the relevant experimental analysis is carried out. The experiment results show that compared with the default settings, the optimized Kubernetes platform can handle more than 2000 concurrent requests, reduce the CPU overhead by more than 1.5%, reduce the memory by more than 0.6%, reduce the average request time by an average of 7.6%, and reduce the number of request failures by at least 32.4%, achieving the expected effect.

Suggested Citation

  • Subrota Kumar Mondal & Zhen Zheng & Yuning Cheng, 2024. "On the Optimization of Kubernetes toward the Enhancement of Cloud Computing," Mathematics, MDPI, vol. 12(16), pages 1-26, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2476-:d:1453841
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    References listed on IDEAS

    as
    1. Subrota Kumar Mondal & Xiaohai Wu & Hussain Mohammed Dipu Kabir & Hong-Ning Dai & Kan Ni & Honggang Yuan & Ting Wang, 2023. "Toward Optimal Load Prediction and Customizable Autoscaling Scheme for Kubernetes," Mathematics, MDPI, vol. 11(12), pages 1-30, June.
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