Causal analysis of network logs with layered protocols and topology knowledge

Satoru Kobayashi, Kazuki Otomo, K. Fukuda
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引用次数: 13

Abstract

To detect root causes of failures in large-scale networks, we need to extract contextual information from operational data automatically. Correlation-based methods are widely used for this purpose, but they have a problem of spurious correlation, which buries truly important information. In this work, we propose a method for extracting contextual information in network logs by combining a graph-based causal inference algorithm and a pruning method based on domain knowledge (i.e., network protocols and topologies). Applying the proposed method to a set of log data collected from a nation-wide R & E network, we demonstrate that the pruning method reduced processing time by 74% compared with a single-handed causal analysis method, and it detected more useful information for troubleshooting compared with an existing area-based method.
利用分层协议和拓扑知识对网络日志进行原因分析
为了检测大规模网络故障的根本原因,我们需要从运行数据中自动提取上下文信息。基于相关性的方法被广泛用于此目的,但它们存在虚假相关性的问题,这掩盖了真正重要的信息。在这项工作中,我们提出了一种通过结合基于图的因果推理算法和基于领域知识(即网络协议和拓扑)的修剪方法来提取网络日志中上下文信息的方法。将所提出的方法应用于从全国r&e网络收集的一组日志数据,我们表明,与单手因果分析方法相比,修剪方法减少了74%的处理时间,并且与现有的基于区域的方法相比,它可以检测到更多有用的故障排除信息。
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