论文标题
美女与野兽：关于数据密集型集装云应用程序性能原型制作的案例研究
Beauty and the beast: A case study on performance prototyping of data-intensive containerized cloud applications
论文作者
论文摘要
基于数据密集型容器的云应用程序在物联网域中的用例中变得很受欢迎。在工程应用程序以满足质量要求时，就会出现挑战，既有经典性能，诸如弹性和弹性和弹性之类的质量要求。在制作可能受益于研究社区的这些应用程序时，缺乏参考用例，应用和经验。此外，很难生成现实可靠的工作负载，以根据规范行使资源。因此，设计在这种环境中表现出相似性能行为的参考应用程序很难。在本文中，我们介绍了针对参考用例和针对具有工业动机的数据密集型容器化云应用程序的应用程序。此外，为了生成可靠的CPU工作负载，我们利用ProtoCom（用于生成资源需求的众所周知的库），并在中等尺寸的Kubernetes群中报告了在各种质量要求下的性能。最后，我们介绍假设特定自动化策略的当前解决方案的可扩展性。在云环境中执行时，校准的结果显示了原始库的较高可变性。我们观察到节点的占用与执行时间的相对可变性之间存在适度的关联。
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.
