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维护良好的交互体验，同时降低云使用成本。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，极大地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，并在无服务器应用程序中调试性能问题。本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的问题，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部署成本，并提高使用无服务器云的组织（如教育、研究和医疗机构）的性能满意度。本研究通过设计新颖的性能测试框架来解决无服务器计算提出的精确性能测试的可扩展性和工作流挑战，该框架将利用相关感知、非参数统计工具，包括聚类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，具体用于：1)通过利用来自同步调用的应用程序性能测试数据中的相关性（即应用程序内部相关性），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（即应用程序间的相关性）；3)通过利用应用程序内部和应用程序之间的相关性，对无服务器应用程序工作流的整体性能进行集成测试。该项目开发的见解和技术也将加深研究界对无服务器计算性能特征的理解，以及如何使用现代统计工具有效地分析无服务器性能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.