ClairvoyantEdge: Prescient Prefetching of On-demand Video at the Edge of the Network

Abstract

On-demand video contributes a large fraction of the data traffic on mobile networks. This share is expected to increase even more drastically in the coming years. While the cellular infrastructure is continuously evolving to keep pace with this increasing demand, it is necessary to ensure that sufficient bandwidth is reserved for other latency-sensitive realtime applications like video conferencing and multiplayer video games. A tangible approach involves reducing on-demand video load on cellular networks, especially from users on the move. We see an opportunity for cellular load reduction using edge nodes based on two observations: (1) video streaming is mostly a download-only operation with sequential data access; and (2) short-range mmWave links can deliver an extremely high throughput for nearby recipients of data. The knowledge of the user's planned travel route creates opportunities for prescient prefetching and delivering the content as the vehicle passes through just in time, using mmWave devices on en route edge nodes. ClairvoyantEdge is a novel networked system infrastructure that leverages inter-edge node communication and the knowledge of users' trajectories to plan and deliver buffered video segments to the vehicles passing by. To evaluate ClairvoyantEdge, we built a comprehensive end-to-end emulation-based workflow that incorporates in situ field measurements of mmWave links into our own homegrown emulation framework. With a minuscule 0.12% coverage of a 46km2 geographical area employing 20 edge nodes distributed in that area providing short-range mmWave access to passing vehicles, we achieve an average reduction of up to 21% in cellular bandwidth usage for video downloads, using a real-world workload comprising 758 vehicles. Our results validate the promise of ClairvoyantEdge for incorporation in future edge infrastructure evolution.