Diagnosing User-Visible Performance Problems in Production High-Density Wi-Fi Networks

Large-scale, high-density Wi-Fi networks use hundreds of access points to serve thousands of closelypacked users within a large physical space (hundreds of thousands of square feet or more, such as in a stadium or arena). Because of their scale, these are complex and dynamic systems comprised of several layers and multiple components within each layer, and faults may be present in any one of these components. The problems that manifest from these faults are usually not network-wide and may be localized to a certain physical areas of the network. This makes these problems challenging to detect and diagnose; in most cases, only a small number of devices tend to be impacted by any given problem. However, many such problems may occur simultaneously in different areas of the network. Adding to the complexity is the dynamic nature of such networks, where the physical positions of radios (in end-user devices), human bodies, and other objects in the space are constantly changing, thereby creating a continually-changing RF environment. Taken together, these properties make problem diagnosis in large-scale, high-density Wi-Fi networks challenging. There are many existing techniques for diagnosing problems in Wi-Fi networks. Many of these approaches rely on data from only a single perspective of the network to diagnose problems, for example, either the client, the infrastructure (access points), or external Wi-Fi sensors that passively monitor the network. In addition, many of these approaches require the invasive modification of the network’s components in order to collect data, through techniques such as the installation of specialized software on clients, modifying the firmware on access points, or even physically installing specialized devices in the RF environment of the Wi-Fi network. Finally, many approaches rely on offline analysis of the collected instrumentation, in which case diagnosis cannot be done in real time (minutes or less). Many others require network connectivity for real-time diagnosis, in which case the device must be able to communicate using the Wi-Fi infrastructure (that may be experiencing a problem). As a result, many of these approaches are difficult to deploy in production networks (due to the high financial cost or maintenance effort required), and those that are deployed often fail to detect and diagnose problems that are localized to a small number of devices (10 or less) or problems that are only present for a short time (minutes or less). This dissertation takes a unique approach that contrasts with existing approaches in three key ways. First, we combine the Wi-Fi performance data from multiple layers of the Wi-Fi network and attempt to diagnose problems at all of these layers, rather than focusing on a single layer alone, and we introduce a fault model that includes faults that can occur across all layers of the system. Second, we require no invasive modification of the Wi-Fi network or its components in order collect data and perform problem diagnosis and mitigation. Third, we present an infrastructure-free approach to problem diagnosis that