Towards automatic troubleshooting for user-level performance degradation in cellular services
Towards automatic troubleshooting for user-level performance degradation in cellular services
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DOI：
10.1145/3495243.3560535
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Xiaofeng Shi;M. Osinski;Chen Qian;Jia Wang
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文献类型：
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作者：
Xiaofeng Shi;M. Osinski;Chen Qian;Jia Wang
在每个UE（用户设备）级别对蜂窝服务问题进行故障排除是蜂窝提供商的基本任务。然而，在每个UE级别诊断服务问题是昂贵的，因为它需要先进的专业知识和对大量网络日志数据的深入检查。本文介绍了NetExp，一个通用的和全面的数据驱动的方法来自动排除客户报告的蜂窝服务问题。NeTExp通过深度神经网络，从海量网络日志数据中提取复杂的时空特征图谱，判断用户报告的服务问题的根本原因是来自网络侧还是设备侧。该系统的训练和验证使用了广泛的网络和客户服务数据从一个主要的蜂窝服务提供商在美国。我们还提供了一个关于2020年导致蜂窝服务问题的外部事件的案例研究，以证明NeTExp在检测网络问题和识别每个UE级别的网络问题相关根本原因方面的有效性。
Troubleshooting cellular service issues at the per-UE (User Equipment) level is an essential task for cellular providers. However, diagnosing service issues at per-UE level is costly because it requires advanced expertise and in-depth inspection of massive network log data. This paper presents NeTExp, a generic and comprehensive data-driven approach to automatically troubleshoot cellular service issues reported by customers. NeTExp determines whether the root cause of a user-reported service issue is from the network side or the device side through deep neural networks, which extract complex spatial-temporal feature profiles from massive network log data. The system is trained and validated using an extensive period of network and customer care data from a major cellular service provider in United States. We also present a case study on an external event that caused cellular service issues in 2020 to demonstrate the effectiveness of NeTExp on detecting network issues and identifying network-issue-related root causes at per-UE level.