With the increasing scale and complexity of cloud platforms and big-data analytics platforms, it is becoming more and more challenging to understand and diagnose the processing of a service request across multi-layer software stacks of such platforms. One way that helps to deal with this problem is to accurately capture the complete end-to-end execution path of service requests among all involved components. This paper presents REPTrace, a generic methodology for capturing such execution paths in a transparent fashion. Moreover, this paper demonstrates the effectiveness of REPTrace by presenting how REPTrace can be leveraged for knowledge extraction and anomaly detection on the platforms’ request processing. Our experimental results show that, REPTrace enables capturing a holistic view of the request processing across multiple layers of the platforms (which is missing in official documentation) and discovering important undocumented features of the platforms. Fault injection experiments show execution anomalies are detected with 93% precision and 96% recall with aid of REPTrace.

中文翻译：

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捕获请求执行路径以了解服务行为并检测异常而无需代码检测


随着云平台和大数据分析平台的规模和复杂性不断增加，理解和诊断跨此类平台的多层软件堆栈的服务请求的处理变得越来越具有挑战性。有助于解决此问题的一种方法是准确捕获所有涉及的组件之间服务请求的完整端到端执行路径。本文介绍了 REPTrace，这是一种以透明方式捕获此类执行路径的通用方法。此外，本文通过介绍如何利用 REPTrace 进行平台请求处理的知识提取和异常检测，证明了 REPTrace 的有效性。我们的实验结果表明，REPTrace 能够捕获跨平台多个层的请求处理的整体视图（官方文档中缺少），并发现平台的重要未记录功能。故障注入实验表明，借助 REPTrace，检测执行异常的精确度为 93%，召回率为 96%。