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Horizontal Pod Autoscaling in Kubernetes for Elastic Container Orchestration
Thanh-Tung Nguyen1, Yu-Jin Yeom1, Taehong Kim1
1School of Information and Communication Engineering, Chungbuk National University, Cheongju, Chungbuk 28644, Korea.
Sensors (Basel, Switzerland)
|August 23, 2020
Summary
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.
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.
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