CSR: Medium:Collaborative Research:Holistic, Cross-Site, Hybrid System Anomaly Debugging for Large Scale Hosting Infrastructures

CSR：中：协作研究：大规模托管基础设施的整体、跨站点、混合系统异常调试

• 批准号: 1514256
• 负责人: Shan Lu
• 金额: $ 28.2万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514256&HistoricalAwards=false
• 关键词: CSR; Medium; Collaborative; Research; Holistic

Large-scale shared hosting infrastructures such as multi-tenant cloud computing systems have become increasingly popular by allowing users to lease resources on-demand in a cost-effective way. As multiple tenants may share computing resources, hosting infrastructures are complex systems and prone to various system anomalies. Although software developers often perform rigorous offline testing, many subtle bugs only manifest themselves during large-scale production run. Many anomalies such as those where the system does not crash but fails to behave as expected are hard to reproduce and diagnose using existing techniques. Existing system anomaly diagnosis work can be broadly classified into two categories: 1) the black-box schemes which do not require source code and are suitable for online production-site diagnosis, and 2) the white-box schemes which require source code and expensive code instrumentation and are suitable for development site, offline diagnosis. Although white-box schemes provide fine-grained diagnosis, large-scale production hosting infrastructures are reluctant to adopt them due to their high-overhead and intrusive system recording approaches.The overarching objective of this project is to explore an innovative cross-site system anomaly debugging approach that intelligently integrates production-site black-box diagnosis with development-site white-box debugging into a more powerful hosting infrastructure debugging framework. This project will develop techniques for development-site, offline white-box debugging that takes the production-site fault inference results as guidance to find the exact anomaly causes. The project will focus on diagnosing non-crashing system anomalies (e.g., performance degradation, service outage, software hang, unexpected halt) that are common in real world hosting infrastructures but are difficult to debug using existing techniques. Techniques developed in this project will generate significant impact on improving the robustness of real world hosting infrastructures. The PIs will develop new course modules on the hosting infrastructure debugging for both graduate and undergraduate classes they regularly teaches. This project will develop programming courseware based on the research prototypes developed in this project. The PIs will use their power of role model and a set of outreach activities to recruit more female students to pursue systems research. The PIs will disseminate their results and collected data broadly through publication and technology transfer. Developed software artifacts and experimental datasets will be released for public use.

大规模共享的托管基础​​架构（例如多租户云计算系统）越来越受欢迎，允许用户以经济高效的方式租赁按需进行按需租赁。由于多个租户可以共享计算资源，因此托管基础架构是复杂的系统，容易出现各种系统异常。尽管软件开发人员经常进行严格的离线测试，但许多微妙的错误只会在大规模生产过程中表现出来。许多异常，例如该系统不会崩溃但无法按预期的表现行为，很难使用现有技术来复制和诊断。现有的系统异常诊断工作可以广泛地分为两类：1）不需要源代码并且适合在线生产站点诊断的黑盒方案，以及2）需要源代码和昂贵代码仪器并且适用于开发现场的白盒方案，离线诊断。 Although white-box schemes provide fine-grained diagnosis, large-scale production hosting infrastructures are reluctant to adopt them due to their high-overhead and intrusive system recording approaches.The overarching objective of this project is to explore an innovative cross-site system anomaly debugging approach that intelligently integrates production-site black-box diagnosis with development-site white-box debugging into a more powerful hosting infrastructure debugging framework.该项目将开发用于开发站点，离线白框调试的技术，以将生产点故障推理结果作为指导，以找到确切的异常原因。该项目将着重于诊断非崩溃系统异常（例如，性能退化，服务中断，软件悬挂，意外停止）在现实世界中托管基础架构的常见，但很难使用现有技术进行调试。该项目开发的技术将对改善现实世界托管基础设施的鲁棒性产生重大影响。 PI将在他们定期教授的研究生和本科课程的托管基础​​设施调试上开发新的课程模块。该项目将根据该项目中开发的研究原型开发编程课程。 PI将利用其榜样的力量和一系列外展活动来招募更多的女学生从事系统研究。 PI将通过出版和技术传输大量传播其结果，并广泛收集数据。开发的软件工件和实验数据集将发布供公众使用。