CableMon: Improving the Reliability of Cable Broadband Networks via Proactive Network Maintenance
CableMon: Improving the Reliability of Cable Broadband Networks via Proactive Network Maintenance
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发表时间：
2020
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通讯作者：
Jiyao Hu;Zhenyu Zhou;Xiaowei Yang;Jacob Malone;Jonathan W. Williams
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作者：
Jiyao Hu;Zhenyu Zhou;Xiaowei Yang;Jacob Malone;Jonathan W. Williams
有线宽带网络是美国为数不多的“最后一英里”宽带技术之一，不幸的是，经过几十年的部署，它们的可靠性很差。有线电视行业提出了主动网络维护（PNM）框架来诊断有线电视网络。然而，关于如何利用这些数据来检测和定位有线网络问题，公众知之甚少，也没有系统的研究。公共领域的现有工具具有高得令人望而却步的假阳性率。在本文中，我们提出了CableMon，这是第一个将机器学习技术应用于PNM数据以提高有线宽带网络可靠性的公共领域系统。CableMon使用统计模型从时间序列数据中生成特征，并使用客户故障单作为提示来推断这些生成特征的异常阈值。我们使用8个月的PNM数据和来自ISP的客户故障单来评估CableMon的性能。我们的结果表明，CableMon检测到的81.9%的异常事件与至少一个客户故障单重叠。这种票证预测的准确性比isp使用的现有公共领域工具高出四倍。CableMon预测的故障单占网络相关故障单总数的23.0%，这表明如果ISP部署CableMon并主动修复CableMon检测到的故障，它可以抢占这些客户呼叫。我们目前的结果，虽然还不成熟，但已经可以切实减少ISP的运营费用，提高客户的体验质量。我们期望未来的工作可以进一步改善这些结果。
Cable broadband networks are one of the few “last-mile” broadband technologies widely available in the U.S. Unfortunately, they have poor reliability after decades of deployment. Cable industry proposed a framework called Proactive Network Maintenance (PNM) to diagnose the cable networks. However, there is little public knowledge or systematic study on how to use these data to detect and localize cable network problems. Existing tools in the public domain have prohibitive high false-positive rates. In this paper, we propose CableMon, the first publicdomain system that applies machine learning techniques to PNM data to improve the reliability of cable broadband networks. CableMon uses statistical models to generate features from time series data and uses customer trouble tickets as hints to infer abnormal thresholds for these generated features. We use eight-month of PNM data and customer trouble tickets from an ISP to evaluate CableMon’s performance. Our results show that 81.9% of the abnormal events detected by CableMon overlap with at least one customer trouble ticket. This ticket prediction accuracy is four times higher than that of the existing public-domain tools used by ISPs. The tickets predicted by CableMon constitute 23.0% of the total networkrelated trouble tickets, suggesting that if an ISP deploys CableMon and proactively repairs the faults detected by CableMon, it can preempt those customer calls. Our current results, while still not mature, can already tangibly reduce an ISP’s operational expenses and improve customers’ quality of experience. We expect future work can further improve these results.