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Balanced Leader Distribution Algorithm in Kubernetes Clusters.

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  • 1School of Information and Communication Engineering, Chungbuk National University, Cheongju, Chungbuk 28644, Korea.

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Summary
This summary is machine-generated.

This study introduces a novel leader election algorithm for Kubernetes to prevent node bottlenecks by distributing application leaders evenly. Experiments confirm its effectiveness in enhancing cluster performance and reliability for stateful applications.

Keywords:
Kubernetescontainersleader electionload balancingstateful

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Cloud Computing

Background:

  • Containerization simplifies application deployment, with Kubernetes orchestrating containerized applications.
  • Stateful applications in Kubernetes require persistent storage and strong data consistency among replicas.
  • Leader-based consistency mechanisms can cause performance bottlenecks when leaders concentrate on specific nodes.

Purpose of the Study:

  • To address the node bottleneck issue caused by concentrated application leaders in Kubernetes.
  • To propose and validate a new leader election algorithm for improved load distribution.
  • To enhance the scalability and availability of stateful applications in Kubernetes.

Main Methods:

  • Developed a novel leader election algorithm designed for even distribution of application leaders across Kubernetes nodes.
  • Conducted experimental evaluations to compare the proposed algorithm against Kubernetes' default leader election mechanism.
  • Measured performance metrics to assess bottleneck mitigation and overall system efficiency.

Main Results:

  • The proposed leader election algorithm effectively distributes leaders, preventing node concentration and subsequent bottlenecks.
  • Experimental results demonstrate the correctness and superior effectiveness of the new algorithm compared to the default.
  • Improved load balancing leads to enhanced scalability and availability for stateful applications.

Conclusions:

  • The developed leader election algorithm successfully resolves the leader concentration bottleneck in Kubernetes.
  • This approach offers a more robust and efficient solution for managing stateful applications.
  • The findings contribute to optimizing Kubernetes performance for demanding, stateful workloads.