Junchen Jiang, Carnegie Mellon University; Shijie Sun, Tsinghua University; Vyas Sekar, Carnegie Mellon University; Hui Zhang, Carnegie Mellon University and Conviva Inc.
Content providers are increasingly using data-driven mechanisms to optimize quality of experience (QoE). Many existing approaches formulate this process as a prediction problem of learning optimal decisions (e.g., server, bitrate, relay) based on observed QoE of recent sessions. While prediction-based mechanisms have shown promising QoE improvements, they are necessarily incomplete as they: (1) suffer from many known biases (e.g., incomplete visibility) and (2) cannot respond to sudden changes (e.g., load changes). Drawing a parallel from machine learning, we argue that data-driven QoE optimization should instead be cast as a real-time exploration and exploitation (E2) process rather than as a prediction problem. Adopting E2 in network applications, however, introduces key architectural (e.g., how to update decisions in real time with fresh data) and algorithmic (e.g., capturing complex interactions between session features vs. QoE) challenges. We present Pytheas, a framework which addresses these challenges using a group-based E2 mechanism. The insight is that application sessions sharing the same features (e.g., IP prefix, location) can be grouped so that we can run E2 algorithms at a per-group granularity. This naturally captures the complex interactions and is amenable to realtime control with fresh measurements. Using an endto- end implementation and a proof-of-concept deployment in CloudLab, we show that Pytheas improves video QoE over a state-of-the-art prediction-based system by up to 31% on average and 78% on 90th percentile of persession QoE.
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