利用无服务器应用分析框架增强无服务器计算的可观察性

R. Cordingly, Navid Heydari, Hanfei Yu, Varik Hoang, Zohreh Sadeghi, W. Lloyd
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引用次数: 8

摘要

为了提高工作负载性能、资源利用率和底层无服务器功能即服务(FaaS)平台的基础设施的可观察性,我们开发了无服务器应用程序分析框架(SAAF)。SAAF提供了一个支持多种编程语言的可重用框架,开发人员可以利用它来检查性能、资源利用率、可伸缩性,以及商业和开源FaaS平台的功能部署的基础设施指标。为了自动化可重复的FaaS性能实验,我们将FaaS Runner作为多线程的FaaS客户端提供。FaaS Runner提供了一个可编程的客户端,它可以编排超过一千个并发的FaaS函数调用。然后使用ReportGenerator将实验输出聚合到CSV文件中,供流行的数据分析工具使用。SAAF和它的支持工具结合起来可以评估48个不同的指标,以增强无服务器软件部署的可观察性。在这篇教程中,我们描述了SAAF及其支持工具,并提供了可以导出的可观察性见解的示例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Observability of Serverless Computing with the Serverless Application Analytics Framework
To improve the observability of workload performance, resource utilization, and infrastructure underlying serverless Function-as-a-Service (FaaS) platforms, we have developed the Serverless Application Analytics Framework (SAAF). SAAF provides a reusable framework supporting multiple programming languages that developers can leverage to inspect performance, resource utilization, scalability, and infrastructure metrics of function deployments to commercial and open-source FaaS platforms. To automate reproducible FaaS performance experiments, we provide the FaaS Runner as a multithreaded FaaS client. FaaS Runner provides a programmable client that can orchestrate over one thousand concurrent FaaS function calls. The ReportGenerator is then used to aggregate experiment output into CSV files for consumption by popular data analytics tools. SAAF and its supporting tools combined can assess forty-eight distinct metrics to enhance observability of serverless software deployments. In this tutorial paper, we describe SAAF and its supporting tools and provide examples of observability insights that can be derived.
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