Collaborative Research: SHF: Small: Exploiting Performance Correlations for Accurate and Low-cost Performance Testing for Serverless Computing

随着组织越来越多地将其应用程序移植到云服务，而不是使用本地资源，他们面临的挑战是确定如何使用云资源并为其应用程序用户保持良好的交互体验，同时降低他们的云使用成本。无服务器计算通过自动配置和管理服务器并消除云开发人员的负担，大大简化了大规模云应用程序的部署。然而，云开发人员仍然需要为他们的部署确定最具成本效益的云资源分配，并调试他们的无服务器应用程序的性能问题。这项研究的重点是通过解决缺乏性能测试工具来准确确定无服务器云应用程序的性能，为云应用程序开发人员提供准确的性能知识。这项研究的成功可以降低使用无服务器云的组织(如教育、研究和医疗机构)的部署成本并提高性能满意度。这项研究专门解决了无服务器计算对准确性能测试提出的可扩展性和工作流挑战，方法是设计新的性能测试框架，该框架将利用相关感知的非参数统计工具，包括集群块引导和基于回归的引导。它将提供三个准确、低成本和自动化的无服务器性能测试框架，特别用于：1)通过利用来自同时调用的应用程序的性能测试数据中的相关性(即，应用程序内相关性)，对单个无服务器应用程序的性能进行单元测试；2)对两个无服务器应用程序之间的性能相关性进行集成测试(即，应用程序间相关性)；以及3)通过利用应用程序内和应用程序间的相关性，对无服务器应用程序的工作流的整体性能进行集成测试。该项目中开发的见解和技术还将加深研究社区对无服务器计算的性能特征以及如何使用现代统计工具有效分析无服务器性能的理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

As organizations increasingly port their applications to cloud services instead of using local resources, they face the challenge of determining how to use cloud resources and maintain good interactive experiences for their application users while reducing their cloud usage costs. Serverless computing greatly simplifies the deployment of large-scale cloud applications by automatically provisioning and managing the servers and removing this burden from cloud developers. However, cloud developers still need to determine the most cost-effective cloud-resource allocations for their deployments and debug performance issues in their serverless applications. This research focuses on providing cloud-application developers with accurate performance knowledge by addressing the lack of performance testing tools to accurately determine the performance of serverless cloud applications. The success of this research can reduce the deployment costs and improve the performance satisfaction for organizations, such as education, research, and health institutions, that utilize serverless clouds. This research specifically addresses the scalability and workflow challenges that serverless computing sets forth to accurate performance testing through the design of novel performance-testing frameworks that will utilize correlation-aware, non-parametric statistical tools, including clustered-block bootstrap and regression-based bootstrap. It will provide three accurate, low-cost, and automated serverless performance-testing frameworks, specifically for: 1) unit testing for a single serverless application's performance by exploiting the correlation within the application's performance testing data from simultaneous invocations (i.e., intra-application correlation); 2) integration testing for the performance correlation between two serverless applications (i.e., inter-application correlation); and 3) integration testing for the overall performance of a workflow of serverless applications by exploiting both intra- and inter-application correlations. The insights and techniques developed in this project will also deepen the research community's understanding of the performance characteristics of serverless computing and on how to effectively analyze serverless performance using modern statistical tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.