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: 2333326
• 负责人: Curtis Tatsuoka
• 金额: $ 7.5万
• 依托单位: University of Pittsburgh
• 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=2333326&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.

大规模网络基础结构（CI）软件系统中的配置故障排除是由于许多可配置的参数，是一项复杂且昂贵的任务。现有的方法诸如日志挖掘和机器学习分析在这种环境中面临挑战。为了解决这个问题，我们提出了BGT4AUATOCI（使用贝叶斯组测试进行自动化的CI配置故障排除），这是一种开创性的解决方案，利用贝叶斯组测试，即使使用最少的先验知识和测试错误，也可以确保准确的结果。经验丰富的CI操作员可以通过近似的先验知识加快该过程。这项研究旨在彻底改变CI配置故障排除，引入一种高度精确，高效的方法，该方法将优化当前和未来的大型CI系统的利用。这项研究的主要重点是解决大型CI系统内自动化配置故障排除的关键挑战。提出的三倍方法包括：（1）为CI配置故障排除制定贝叶斯组测试，该测试采用晶格模型来准确地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）用于贝叶斯组测试的多项式范式，该范式对同时观察CI系统的多个方面引入了多项式响应，从而实现了有效的测试选择算法，以共同测试影响CIS各个方面的共同测试配置参数。 （3）自动配置故障排除，其中涉及几个关键组件的设计，以建立BGT4AAUTOCI作为自动配置故障排除框架，从而最大程度地减少了对人类干预的需求。该项目的结果有可能显着提高CI配置故障排除的效率和准确性，从而使当前和未来的大规模CI系统受益。这项奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。