Today’s network operators strive to create self-healing cellular networks that have a fully automated troubleshooting management process. To this end, the network monitoring system should be capable of detecting issues, diagnosing them, and triggering the adequate recovery action. In this paper, we propose an unsupervised solution to diagnose the root causes of network issues. As monitoring systems collect a large number of logs from the different devices in their networks, it is possible to determine which connections resulted in a poor user experience and apply a failed/successful label. Our solution, Automatic Root Cause Diagnosis (ARCD), analyzes labeled connection logs to identify the major contributors to the network inefficiency (e.g., a faulty core device) as well as the incompatibilities between different elements (e.g., make and model of a phone not being able to access a service). We evaluate the effectiveness of our solution by using logs from three different real cellular networks. In each case, ARCD was able to identify the major contributors and the most widespread incompatibilities. In the three cases, the precision (detection accuracy) and the recall (detection rate) are higher than 90%.

中文翻译：

---

为移动网络中的根本原因诊断引入无监督自动化解决方案

当今的网络运营商努力创建具有全自动故障排除管理流程的自愈蜂窝网络。为此，网络监控系统应该能够检测问题、诊断问题并触发适当的恢复操作。在本文中，我们提出了一种无监督的解决方案来诊断网络问题的根本原因。由于监控系统从其网络中的不同设备收集大量日志，因此可以确定哪些连接导致用户体验不佳并应用失败/成功标签。我们的解决方案，自动根本原因诊断 (ARCD)，分析标记的连接日志，以确定网络效率低下的主要因素（例如，有故障的核心设备）以及不同元素之间的不兼容性（例如，手机的品牌和型号无法访问服务）。我们通过使用来自三个不同真实蜂窝网络的日志来评估我们的解决方案的有效性。在每种情况下，ARCD 都能够确定主要的贡献者和最普遍的不兼容性。三种情况下，precision（检测准确率）和recall（检测率）均高于90%。