Maestro: QoE-Aware Dynamic Resource Allocation in Wi-Fi Networks

Wi-Fi is an integral part of today's Internet infrastructure, enabling a diverse range of applications and services. Prior approaches to Wi-Fi resource allocation optimized Quality of Service (QoS) metrics, which often do not accurately reflect the user's Quality of Experience (QoE). To address the gap between QoS and QoE, we introduce Maestro, an adaptive method that formulates the Wi-Fi resource allocation problem as a partially observable Markov decision process (PO-MDP) to maximize the overall system QoE and QoE fairness. Maestro estimates QoE without using any application or client data; instead, it treats them as black boxes and leverages temporal dependencies in network telemetry data. Maestro dynamically adjusts policies to handle different classes of applications and variable network conditions. Additionally, Maestro uses a simulation environment for practical training. We evaluate Maestro in an enterprise-level Wi-Fi testbed with a variety of applications, and find that Maestro achieves up to 25× and 78% improvement in QoE and fairness, respectively, compared to the widely-deployed Wi-Fi Multimedia (WMM) policy. Compared to the state-of-the-art learning approach QFlow, Maestro increases QoE by up to 69%. Unlike QFlow which requires modifications to clients, we demonstrate that Maestro improves QoE of popular over-the-top services with unseen traffic without control over clients or servers.