相关实验视频
Updated: Jun 22, 2025

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A Quantitative Fitness Analysis Workflow
Published on: August 13, 2012
14.5K
关于对自动扩展政策和FaaS工作负载的QoS之间的相互关系的分析
Sara Hong1, Yeeun Kim1, Jaehyun Nam2
1Department of Convergence Security Engineering, Sungshin Women's University, 2, Bomun-ro 34da-gil, Seongbuk-gu, Seoul 02844, Republic of Korea.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
这项研究通过分析自动扩展配置来优化无服务器功能即服务 (FaaS) 在Kubernetes (K8s) 上的性能. 调查结果指导了特定于工作负载的扩展策略,以实现延迟服务水平目标 (SLO).
科学领域:
- 云计算 云计算 云计算 云计算
- 分布式系统 分布式系统
- 性能工程是指性能工程.
背景情况:
- 无服务器技术和功能即服务 (FaaS) 提供灵活的云原生应用程序管理.
- 库贝内特 (K8s) 和Knative是常见的无服务器后端,提供自动扩展和资源管理.
- 内部的FaaS平台越来越多地被采用,但缺乏标准化的K8s自动扩展指南.
研究的目的:
- 调查K8s自动扩展配置对FaaS工作负载性能的影响.
- 为优化无服务器资源分配和调度制定指南.
- 解决各种内部FaaS工作负载的服务水平目标 (SLO) 的挑战.
主要方法:
- 在K8s环境中对FaaS工作负载进行了全面的测量研究.
- 分析了自动扩展行为和性能指标 (如延迟) 之间的关系.
- 评估了各种缩放配置,包括基准度量,值和响应计数.
主要成果:
- 根据工作负载特征应用扩展配置的衍生特定逻辑.
- 确定了最佳配置,以最大限度地提高延迟SLO的差异.
- 展示了扩展参数如何影响响应数量和整体服务质量 (QoS).
结论:
- 标准化的K8s自动扩展指南对于内部的FaaS环境至关重要.
- 特定于工作负载的缩放配置显著影响FaaS性能和SLO遵守.
- 拟议的方法有助于评估和改进K8对FaaS的扩展效率.
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