Beauty and the Beast：数据密集型容器化云应用程序性能原型的案例研究
Beauty and the beast: A case study on performance prototyping of data-intensive containerized cloud applications
摘要：
随着物联网领域用例的增加，数据密集型的基于容器的云应用程序变得越来越流行。当设计这些应用程序以满足质量要求时，无论是经典的性能要求还是新兴的弹性和恢复力要求，都面临着挑战。在对这些应用程序进行原型设计时，缺乏参考用例、应用程序和经验，这些应用程序可能会使研究社区受益。此外，很难生成现实和可靠的工作负载，根据规范来使用资源。因此，设计在这样的环境中表现出类似性能行为的参考应用程序是困难的。在本文中，我们提出了一个正在进行的工作，为具有工业动机的数据密集型容器化云应用程序提供参考用例和应用程序。此外，为了生成可靠的CPU工作负载，我们使用ProtoCom，这是一个用于生成资源需求的知名库，并在中等规模的Kubernetes集群中报告各种质量要求下的性能。最后，我们提出了当前解决方案的可扩展性，假设一个特定的自动缩放策略。校准的结果表明，在云环境中执行时，Protocom库的高度可变性。我们观察到一个适度的关联节点的占用率和执行时间的相对变化。
英文摘要：
Data-intensive container-based cloud applications have become popular with the increased use cases in the Internet of Things domain. Challenges arise when engineering such applications to meet quality requirements, both classical ones like performance and emerging ones like elasticity and resilience. There is a lack of reference use cases, applications, and experiences when prototyping such applications that could benefit the research community. Moreover, it is hard to generate realistic and reliable workloads that exercise the resources according to a specification. Hence, designing reference applications that would exhibit similar performance behavior in such environments is hard. In this paper, we present a work in progress towards a reference use case and application for data-intensive containerized cloud applications having an industrial motivation. Moreover, to generate reliable CPU workloads we make use of ProtoCom, a well-known library for the generation of resource demands, and report the performance under various quality requirements in a Kubernetes cluster of moderate size. Finally, we present the scalability of the current solution assuming a particular autoscaling policy. Results of the calibration show high variability of the ProtoCom library when executed in a cloud environment. We observe a moderate association between the occupancy of node and the relative variability of execution time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
