Collaborative Research: EAGER: Automating CI Configuration Troubleshooting with Bayesian Group Testing

协作研究：EAGER：使用贝叶斯组测试自动化 CI 配置故障排除

• 批准号: 2333324
• 负责人: Xiaoyi Lu
• 金额: $ 15万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333324&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Automating; CI

Configuration troubleshooting in large-scale cyberinfrastructure (CI) software systems is a complex and costly task due to numerous configurable parameters. Existing methods like log mining and machine learning analysis face challenges in such environments. To address this, we present BGT4AutoCI (Automating CI Configuration Troubleshooting with Bayesian Group Testing), a groundbreaking solution that leverages Bayesian Group Testing, ensuring accurate results even with minimal prior knowledge and testing errors. Experienced CI operators can expedite the process with approximated prior knowledge. This research aims to revolutionize CI configuration troubleshooting, introducing a highly precise and efficient approach that will optimize the utilization of current and future large-scale CI systems.The primary focus of this research is to address critical challenges in automated configuration troubleshooting within large-scale CI systems. The proposed three-fold approach encompasses: (1) Formulating Bayesian Group Testing for CI configuration troubleshooting, which employs lattice models to accurately identify risks at the individual configuration parameter level, taking uncertainty into account during troubleshooting. (2) A multinomial paradigm for Bayesian Group Testing, which introduces multinomial responses to simultaneously observe multiple aspects of CI systems, enabling efficient test selection algorithms for jointly testing configuration parameters that impact various aspects of CIs. (3) Automated configuration troubleshooting, which involves the designs of several key components to establish BGT4AutoCI as an automated configuration troubleshooting framework that minimizes the need for human intervention. The outcomes of this project hold the potential to significantly enhance the efficiency and accuracy of CI configuration troubleshooting, benefiting current and future large-scale CI systems.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.

由于可配置参数众多，大规模网络基础设施 (CI) 软件系统中的配置故障排除是一项复杂且成本高昂的任务。日志挖掘和机器学习分析等现有方法在此类环境中面临挑战。为了解决这个问题，我们推出了 BGT4AutoCI（使用贝叶斯组测试自动进行 CI 配置故障排除），这是一种利用贝叶斯组测试的突破性解决方案，即使在先验知识和测试错误最少的情况下也能确保准确的结果。经验丰富的 CI 操作员可以利用近似的先验知识来加快这一过程。这项研究旨在彻底改变 CI 配置故障排除，引入一种高精度和高效的方法，以优化当前和未来的大规模 CI 系统的利用率。这项研究的主要重点是解决大规模自动化配置故障排除的关键挑战持续集成系统。所提出的三重方法包括：（1）制定用于 CI 配置故障排除的贝叶斯组测试，该测试采用格模型来准确识别单个配置参数级别的风险，并在故障排除期间考虑不确定性。 (2) 贝叶斯组测试的多项式范式，引入多项式响应来同时观察 CI 系统的多个方面，从而实现有效的测试选择算法来联合测试影响 CI 各个方面的配置参数。 (3)自动化配置故障排除，涉及几个关键组件的设计，以将BGT4AutoCI建立为自动化配置故障排除框架，最大限度地减少人工干预的需要。该项目的成果有可能显着提高 CI 配置故障排除的效率和准确性，使当前和未来的大型 CI 系统受益。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持以及更广泛的影响审查标准。