CAREER: Principled yet practical observability for a microservices-based cloud

社会几乎在日常生活的方方面面都依赖于使用微服务架构构建的基于云的软件服务-例如，购物、看电影和工作。尽管微服务体系结构有很多优点，但它有一个严重的缺点。观察服务如何处理用户请求(例如，购买一本书)是极具挑战性的，因为它们涉及许多更简单的(微)服务之间的无数交互。这种可观察性的缺乏使重要的管理任务复杂化，例如问题诊断和资源管理。以前的研究已经证明了分布式跟踪的强大潜力-捕捉微服务如何交互以处理请求的图表-提供微服务可观察性。但是，在现实世界中的结果一直令人失望。潜在和现实之间的差距之所以会出现，是因为研究工作假设了捕捉各种行为且没有数据丢失的原则性跟踪图。但是，在实践中，服务从来没有很好地利用过，数据丢失是很常见的。这项提议的首要目标是创建一个新的跟踪平台，自动推断使跟踪工作原则化所需的数据。这样做将实现分布式跟踪在微服务可观察性方面的巨大潜力，提高现有基于跟踪的管理工具的效用，并启用变革性的新工具。这些结果将提高社会所依赖的软件服务的弹性和效率。来自这个项目的见解将为大学中关于调试云系统的课程材料和项目、高中研究计划和中学推广计划提供适合年龄的课程材料和项目。该项目提出了一个新颖的跟踪平台，它使用两组基元自动丰富基于SPAN的跟踪。1)并发/等待之前发生的基元和2)基元上的空洞和空洞。前者允许识别请求的关键路径，从而实现松弛分析、有针对性的性能调试和精确的资源分配决策。后者允许在跟踪中表示数据丢失的区域，并预测在这些区域中可能执行什么工作。由于调度决策可能会混淆因果结构，因此该项目将研究主动探测方法，以梳理出并发和等待关系。为了解决不确定性，它将探索表示原语的概率数据模型。该项目将通过修改现有的自动伸缩解决方案和性能调试工具来演示原语的价值，以使用它们。它还将演示一种新的迹子图采样方法，该方法由孔洞和孔洞覆盖基元实现。建议的平台、改进的管理工具和推理方法将公开提供，使计算机科学和微服务可观察性社区受益。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Society relies on cloud-based software services built using the microservices architecture in almost every aspect of their everyday lives---e.g., to shop, watch movies, and work. Though the microservices architecture has many advantages, it has one critical drawback. Observing how user requests (e.g., to buy a book) are processed by services is extremely challenging because they involve myriad interactions among many simpler (micro)services. This lack of observability complicates important management tasks, such as problem diagnosis and resource management. Previous research has demonstrated the strong potential of distributed tracing---which captures graphs of how microservices interact to process requests---to provide microservice observability. But, results in real-world settings have been disappointing. This gap between potential and reality occurs because research efforts assume principled trace graphs that capture a variety of behaviors and have no data loss. But, in practice, services are never well-instrumented and data loss is common. The overarching goal of this proposal is to create a new tracing platform that automatically infers the data needed to make traces principled. Doing so will actualize distributed tracings' vast potential for microservice observability, improve the utility of existing tracing-based management tools, and enable transformative new tools. These outcomes will improve the resiliency and efficiency of the software services society depends on. Insights from this project will inform age-appropriate course material and projects in a college course on debugging cloud systems, a high-school research program, and a middle-school outreach program.This project proposes a novel tracing platform that automatically enriches span-based traces with two sets of primitives. 1) The happens-before concurrency/wait primitives and 2) the holes and holes covering primitives. The former allows requests' critical paths to be identified, enabling slack analyses, targeted performance debugging, and precise resource allocation decisions. The latter allows areas of data loss to be expressed in traces along with predictions of what work might execute in them. Since scheduling decisions may obfuscate causal structure, the project will investigate active probing methods to tease out concurrent and waiting relationships. To account for uncertainty, it will explore probabilistic data models to represent the primitives. The project will demonstrate the value of the primitives by modifying an existing auto-scaling solution and performance-debugging tool to use them. It will also demonstrate a new trace subgraph sampling approach made possible by the holes and holes covering primitive. The proposed platform, improved management tools, and inference methods will be publicly available to benefit the computer science and microservice observability communities.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.