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

协作研究：SHF：小型：利用性能相关性对无服务器计算进行准确且低成本的性能测试

• 批准号: 2155096
• 负责人: Wei Wang
• 金额: $ 32.93万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2155096&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Small; Exploiting; Collaborative; Research; SHF; Small; Exploiting

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.

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时，他们面临着确定如何使用云资源并维持其应用程序用户的良好交互体验的挑战，同时降低了其云使用成本。 无服务器计算通过自动配置和管理服务器并从云开发人员那里消除这一负担来大大简化大规模云应用程序的部署。 但是，云开发人员仍然需要确定其部署的最具成本效益的云资源分配以及在其无服务器应用程序中的调试性能问题。这项研究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，从而为云应用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，研究和卫生机构等组织的绩效满意度。这项研究专门解决了通过设计新型性能测试框架的设计，可以利用相关性，非参数统计工具，包括群集的bootstrap和基于回归的bootstrap，这些挑战旨在通过设计新型性能测试框架进行精确的性能测试。它将通过以下三个准确，低成本和自动化的无服务器性能测试框架，专门用于：1）通过利用应用程序的性能测试数据中的相关性，从同时调查（即，Intraplication相关性）来利用单个无服务器应用程序的性能进行单位测试； 2）对两个无服务器应用程序之间性能相关性的集成测试（即应用间相关性）； 3）通过利用内部和插之间的相关性来利用无服务器应用程序工作流程的整体性能的集成测试。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的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响来审查审查标准来通过评估来通过评估来获得支持的。