CNS Core: Small: Collaborative Research: Improving the Reliability of Cable Broadband Networks with Proactive Network Maintenance

高速互联网接入在现代生活中扮演着重要的角色。它有助于缩小数字鸿沟，实现电子商务，并为远程工作、学习和娱乐提供机会。在美国，有线互联网服务提供商(ISP)是为数不多的能够为美国家庭提供高速互联网接入的基础设施之一，在许多农村地区，它们往往是唯一的宽带选择。然而，大量的测量研究表明，宽带接入网的可靠性较差。(有线电视网的许多部分现在已经有几十年的历史了。)这项工作旨在解决这一问题。如果成功，它可以显著提高有线宽带网络的可靠性，从而改善互联网的可用性和数百万有线宽带用户的体验质量。提高有线网络的可靠性是具有挑战性的，因为连接用户家中和互联网的最后一英里链路往往是由同轴电缆组成的。他们容易受到无线电频率干扰，并受到与衰老有关的问题的困扰。这些问题可能会表现为无法恢复的嘈杂信号，扰乱用户的互联网连接。有线电视行业目前从用户的有线调制解调器收集性能数据。然而，对于如何利用这些数据来检测和定位网络问题并对问题的严重程度进行评级，缺乏系统的研究。现有技术在实践中经常导致不可接受的高数量的假阳性。这项工作将开发算法和工具，以帮助检测和定位有线网络中的故障，并估计网络故障的性能影响。具体地说，这项工作将包括1)使用收集的数据准确检测有线网络内部故障的算法；2)通过对异常数据模式进行群集来将故障定位到地理位置的算法；以及3)估计给定网络问题可能接到多少电话的算法，以此作为确定维修优先级的机制。在这个项目中，这些算法将在软件中实现。这项工作产生的所有算法和代码都将公开提供。研究人员预计，该项目中开发的算法也适用于WiFi和蜂窝网络。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

High-speed Internet access plays an important role in modern life. It helps narrow the digital divide, enables e-commerce, and provides opportunities for remote work, study, and entertainment. In the US, cable Internet Service Providers (ISPs) are one of the few available infrastructures that can provide high-speed Internet access to US homes, and in many rural areas, they are often the only broadband choice. However, much measurement study has shown that broadband access networks have poor reliability. (Many parts of the cable networks are now decades old.) This work aims to address this problem. If successful, it can significantly improve the reliability of cable broadband networks, thereby improving Internet availability and quality of experience of millions of cable broadband users.It is challenging to improve the reliability of cable networks because the "last-mile" links that connect a subscriber's home to the Internet are often made of coaxial cables. They are vulnerable to radio frequency interference and suffer from aging-related issues. These problems can manifest themselves as unrecoverable noisy signals, disrupting a subscriber's Internet connectivity. The cable industry currently collects performance data from users' cable modems. However, there is a lack of systematic study on how to use this data to detect and localize network problems and to rank the severity of problems. Existing techniques often lead to an unacceptably high number of false positives in practice. This work will develop algorithms and tools that can help detect and localize faults in cable networks and estimate the performance impact of network faults. Specifically, the work will include 1) algorithms that use collected data to accurately detect faults inside a cable network; 2) algorithms that localize a fault to a geographical location by clustering anomalous data patterns; and 3) algorithms that estimate how many calls an ISP may receive given a network problem as a mechanism to prioritize repairs. In this project, these algorithms will be implemented in software. All algorithms and code resulting from this work will be made publicly available. The researchers anticipate that the algorithms developed in this project are also applicable to WiFi and cellular networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.