CAREER: Auto-generated experimentation for performance diagnosis of distributed systems

职业：自动生成分布式系统性能诊断实验

• 批准号: 2239291
• 负责人: Timothy Zhu
• 金额: $ 59.94万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239291&HistoricalAwards=false
• 关键词: CAREER; Auto; generated; experimentation; performance

Debugging, the process of finding and fixing problems in systems, is one of the most critical and time-consuming activities for computer scientists. This research focuses on performance debugging, one of the most challenging forms of debugging. Performance debugging is difficult because slowdowns typically do not break functionality in easily identifiable locations. Diagnosing where the slowdowns are, and their causes, requires gathering and analyzing detailed performance measurements. This is particularly challenging for slowdowns that only appear sporadically or only affect a fraction of the workload. Coupled with the fact that many large and small companies build distributed systems composed of hundreds to thousands of services/components, it is no surprise that companies often need to hire teams of specialized performance engineers to track down the main performance issues. The goal of this research is to develop new tools and methodologies for automatically diagnosing performance issues within distributed systems. Rather than identifying faulty or misbehaving components, this research tackles the harder problem of identifying fundamental inefficiencies within the design and implementation of a system. The research will pioneer a novel diagnosis approach that auto-generates experiments to validate or refute performance hypotheses. Experiments generated based on these hypotheses will be used to progressively narrow down the problem scope and identify the root cause(s) of slowdowns. The resulting tools will provide engineers insights into where and what to investigate so that their efforts will be focused on fixing problems rather than diagnosing them.The direct benefit of this research is in developing new automated performance diagnosis methodologies and open-sourced tools for assisting both general software developers and specialized performance engineers in finding sources of slowdowns in their systems. This saves costly engineering time and could help engineers build more cost- and energy-efficient systems. By integrating code analysis and performance modeling principles into the automated tool, the ideas from this research are more easily accessible to a broader base of engineers that might not otherwise have this specialized knowledge. To have a lasting effect on debugging methodologies and practices, this project also includes a significant education component that aims to transform debugging education in undergraduate curricula through (i) developing a new debugging course, where concepts from this research will be integrated as a course module; and (ii) creating a teaching assistant module for training teaching assistants on how to teach debugging.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.

调试是在系统中查找和解决问题的过程，是计算机科学家最关键和耗时的活动之一。这项研究重点是性能调试，这是最具挑战性的调试形式之一。性能调试很困难，因为放缓通常不会在易于识别的位置中破坏功能。诊断放缓的位置及其原因需要收集和分析详细的绩效测量。对于仅偶尔出现或仅影响工作量的一小部分的放缓而言，这尤其具有挑战性。再加上许多大型和小型公司建立由数百至数千个服务/组件组成的分布式系统，公司通常需要聘请专业绩效工程师团队来追踪主要绩效问题也就不足为奇了。这项研究的目的是开发新的工具和方法，以自动诊断分布式系统内的性能问题。这项研究没有确定错误或不当行为的组成部分，而是解决了系统的设计和实施中识别基本效率低下的更困难的问题。这项研究将开创一种新型诊断方法，该方法自动生成实验以验证或反驳绩效假设。基于这些假设生成的实验将用于逐步缩小问题范围并确定放缓的根本原因。最终的工具将为工程师提供有关在哪里和要研究的内容，以便他们的努力专注于解决问题而不是诊断问题。这项研究的直接好处在于开发新的自动化性能诊断方法和开源工具，以帮助一般软件开发人员和专业绩效工程师在其系统中找到速度降低的来源。这样可以节省昂贵的工程时间，并可以帮助工程师建立更具成本和节能的系统。通过将代码分析和性能建模原理集成到自动化工具中，这项研究的想法更容易获得更广泛的工程师基础，这些工程师可能没有这些专业知识。为了对调试方法和实践产生持久的影响，该项目还包括一个重要的教育部分，旨在通过（i）开发新的调试课程来改变本科课程中的调试教育，该课程将在该课程中将其整合为课程模块； （ii）创建一个助教模块，以培训如何教授调试的助教。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被视为值得通过评估来获得支持。