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

在数据中心中运行的分布式应用程序对社会至关重要（例如，用于购物、银行业务）。工程师必须快速诊断和修复在数据中心观察到的问题；然而，这样做是极具挑战性的。一个重要的障碍是工程师必须花费大量的时间和精力来探索需要什么样的工具（例如，关于特定应用程序行为的日志消息）来提供对新问题的可见性。为了在这方面提供帮助，该项目将开发一个仪器框架，在响应新问题时，该框架将自动搜索可能的仪器选择空间，并启用所需的仪器来提供洞察。这个项目解决了与创建自动仪器框架相关的基本挑战：(a)什么算法和启发式适合于自动和有效地探索仪器搜索空间？(b)在框架内需要什么架构支持才能进行自动探索？(c)如何在不显著影响应用程序性能的情况下探索搜索空间？该提案将探索基于算子知识、统计学和机器学习的算法的效用，以探索搜索空间。它将建立在端到端跟踪的基础上，因为这将使框架能够处理影响不同请求集的问题。该项目将为下一代仪器框架的体系结构提供信息，这些框架需要跟上分布式应用程序日益增长的复杂性。在评估框架时，在流行的开源分布式应用程序中发现的关键问题将提高它们的健壮性。研究人员将能够利用该项目发布的软件构件来创建利用框架独特功能的新型分布式应用程序管理工具。他们将能够在研究云中部署框架，以从中获得有价值的工作负载跟踪。该项目将生成分布式应用程序的诊断实践课程模块。这个项目产生的工件，包括框架源代码、工作负载跟踪、仪器化应用程序和研究结果，将在https://massopen.cloud和https://www.rajasambasivan.com上免费在线发布。所有软件工件也将存储在Github中。从项目开始，所有的工件将至少在7年内可用。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.