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.

查找和修复系统中的问题的过程是计算机科学家最关键和最耗时的活动之一。本研究的重点是性能调试，调试的最具挑战性的形式之一。性能调试是困难的，因为减速通常不会在容易识别的位置中断功能。要诊断哪里出现了速度减慢及其原因，需要收集和分析详细的性能度量。这对于偶尔出现或只影响一小部分工作负载的减速来说尤其具有挑战性。再加上许多大型和小型公司构建由数百到数千个服务/组件组成的分布式系统，因此公司经常需要聘请专业的性能工程师团队来跟踪主要的性能问题也就不足为奇了。本研究的目标是开发新的工具和方法，自动诊断分布式系统中的性能问题。而不是识别故障或行为不端的组件，这项研究解决了更难的问题，确定系统的设计和实现中的基本效率低下。该研究将开创一种新的诊断方法，自动生成实验来验证或反驳性能假设。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减速的根本原因。由此产生的工具将为工程师提供深入了解在哪里和什么调查，使他们的努力将集中在修复问题，而不是诊断them.The直接的好处，这项研究是在开发新的自动化性能诊断方法和开源工具，以协助一般的软件开发人员和专业的性能工程师在他们的系统中找到减慢的来源。这节省了昂贵的工程时间，并可以帮助工程师构建更具成本效益和能源效益的系统。通过将代码分析和性能建模原则集成到自动化工具中，这项研究的想法更容易被更广泛的工程师所接受，否则他们可能不会有这种专业知识。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组成部分，旨在通过以下方式改变本科课程中的调试教育：（i）开发一门新的调试课程，将本研究的概念整合为课程模块;以及（ii）创建一个教学助理模块，用于培训教学助理如何教授调试。该奖项反映了NSF的法定使命，并已被视为通过使用基金会的知识价值和更广泛的影响审查标准进行评估，