CSR: Small: A Just-in-Time, Cross-Layer Instrumentation Framework for Diagnosing Performance Problems in Distributed Applications

CSR：小型：用于诊断分布式应用程序中性能问题的即时跨层仪表框架

基本信息

批准号：
1815323
负责人：
Raja Sambasivan
金额：
$ 46.02万
依托单位：
Trustees of Boston University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2020-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815323&HistoricalAwards=false
关键词：
CSR Small Just Time Cross

项目摘要

Distributed applications running in data centers are critical to society (e.g., for shopping, banking). Engineers must diagnose and fix problems observed in data centers quickly; however, doing so is extremely challenging. A significant hurdle is that engineers must spend significant time and effort exploring what instrumentation (e.g., log messages about specific application behaviors) is needed to provide visibility into a new problem. To assist in this front, this project will develop an instrumentation framework that, in response to a new problem, will automatically search the space of possible instrumentation choices and enable the instrumentation needed to provide insight into it.This project addresses fundamental challenges associated with creating an automatic instrumentation framework: (a) What algorithms and heuristics are suited for automatically and efficiently exploring the instrumentation search space? (b) What architectural support is needed within the framework to enable automatic exploration? (c) How can the search space be explored without significantly impacting application performance? The proposal will explore the utility of algorithms based on operator knowledge, statistics, and machine learning to explore the search space. It will build on end-to-end tracing, as this will enable the framework to work for problems that affect different sets of requests.This project will inform the architecture of next-generation instrumentation frameworks, which are needed to keep pace with the ever-increasing complexity of distributed applications. The critical issues identified in popular open-source distributed applications while evaluating the framework will improve their robustness. Researchers will be able to leverage the software artifacts released by this project to create novel distributed-application-management tools that leverage the framework's unique capabilities. They will be able to deploy the framework in research clouds to obtain valuable workload traces from them. The project will generate course modules on diagnosis practices for distributed applications.The artifacts produced by this project, including framework source code, workload traces, instrumented applications, and research results, will be freely disseminated online at: https://massopen.cloud and https://www.rajasambasivan.com. All software artifacts will be stored in Github as well. All artifacts will be available for a minimum of seven years from the start of the project.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.

在数据中心运行的分布式应用程序对社会至关重要（例如，用于购物，银行业）。工程师必须迅速诊断并解决数据中心中观察到的问题；但是，这样做非常具有挑战性。一个重大的障碍是，工程师必须花费大量时间和精力探索需要哪种仪器（例如，有关特定应用程序行为的日志消息）才能提供新问题的可见性。为了在这方面提供帮助，该项目将开发一个仪器框架，以应对新问题，将自动搜索可能的仪器选择的空间，并启用所需的仪器以提供洞察力。该项目解决了与创建自动仪器框架相关的基本挑战：（a）什么算法和HERISTISS可以自动探索什么算法和有效地探索仪器？（b）框架内需要哪些建筑支持才能自动探索？（c）如何探索搜索空间而不会显着影响应用程序性能？该提案将根据操作员知识，统计信息和机器学习来探索算法的实用性，以探索搜索空间。它将以端到端的跟踪为基础，因为这将使框架能够解决影响不同请求集的问题。本项目将为下一代仪器框架的体系结构提供信息，这需要与分布式应用程序的不断增长的复杂性保持同步。在评估框架的同时，在流行的开源分布式应用程序中确定的关键问题将提高其稳健性。研究人员将能够利用该项目发布的软件工件，以创建新颖的分布式应用管理工具来利用框架的独特功能。他们将能够在研究云中部署框架，从中获得有价值的工作负载跟踪。该项目将生成有关分布式应用程序诊断实践的课程模块。该项目生产的工件，包括框架源代码，工作负载轨迹，仪器应用程序和研究结果，将在以下网址在线自由传播：所有软件工件也将存储在Github中。从项目开始后，所有工件将至少有7年的时间。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，被认为值得通过评估来获得支持。