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Horizontal Pod Autoscaling in Kubernetes for Elastic Container Orchestration.

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Summary
This summary is machine-generated.

This study explores Kubernetes Horizontal Pod Autoscaler (HPA) performance, comparing default resource metrics with custom metrics from Prometheus. It offers insights for optimizing HPA in containerized environments.

Keywords:
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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • Kubernetes is an open-source container orchestration platform.
  • Autoscaling mechanisms like Horizontal Pod Autoscaler (HPA) ensure high availability and scalability.
  • HPA dynamically adjusts the number of pods without system restarts.

Purpose of the Study:

  • To investigate the operational behaviors of Kubernetes HPA through experiments.
  • To analyze the performance differences between Kubernetes Resource Metrics (KRM) and Prometheus Custom Metrics (PCM) for HPA.
  • To provide optimization strategies for HPA performance.

Main Methods:

  • Experimental investigation of HPA.
  • Comparison of KRM and PCM in affecting HPA performance.
  • Analysis of HPA operational behaviors.

Main Results:

  • HPA performance is influenced by the type of metrics used (KRM vs. PCM).
  • Detailed experimental data on HPA's dynamic scaling capabilities.
  • Identification of factors affecting HPA efficiency.

Conclusions:

  • Understanding the differences between KRM and PCM is crucial for effective HPA tuning.
  • Optimizing HPA requires careful consideration of metric sources and configurations.
  • The findings offer practical guidance for researchers, developers, and administrators using Kubernetes.