Towards Trouble-Free Networks for End Users

Network applications and Internet services fail all too frequently. However, end users cannot effectively identify the root cause using traditional troubleshooting techniques due to the limited capability to distinguish failures caused by local network elements from failures caused by elements located outside the local area network. To overcome these limitations, we propose a new approach, one that leverages collaboration of user machines to assist end users in diagnosing various failures related to Internet connectivity and poor network performance. First, we present DYSWIS ("Do You See What I See?"), an automatic network fault detection and diagnosis system for end users. DYSWIS identifies the root cause(s) of network faults using diagnostic rules that consider diverse information from multiple nodes. In addition, the DYSWIS rule system is specially designed to support crowdsourced and distributed probes. We also describe the architecture of DYSWIS and compare its performance with other tools. Finally, we demonstrate that the system successfully detects and diagnoses network failures which are difficult to diagnose using a single-user probe. Failures in lower layers of the protocol stack also have the potential to disrupt Internet access; for example, slow Internet connectivity is often caused by poor Wi-Fi performance. Channel contention and non-Wi-Fi interference are the primary reasons for this performance degradation. We investigate the characteristics of non-Wi-Fi interference that can severely degrade Wi-Fi performance and present WiSlow ("Why is my Wi-Fi slow?"), a software tool that diagnoses the root causes of poor Wi-Fi performance. WiSlow employs user-level network probes and leverages peer collaboration to identify the physical location of these causes. The software includes two principal methods: packet loss analysis and 802.11 ACK number analysis. When the issue is located near Wi-Fi devices, the accuracy of WiSlow exceeds 90%. Finally, we expand our collaborative approach to the Internet of Things (IoT) and propose a platform for network-troubleshooting on home devices. This platform takes advantage of built-in technology common to modern devices --- multiple communication interfaces. For example, when a home device has a problem with an interface it sends a probe request to other devices using an alternative interface. The system then exploits cooperation of both internal devices and remote machines. We show that this approach is useful in home networks by demonstrating an application that contains actual diagnostic algorithms