EdgeURB: Edge-driven Unified Resource Broker for Real-time Video Analytics

Real-time video analytics applications are one of the driving forces towards adoption of edge computing due to the latter’s ability to provide ‘near cloud-scale’ resources closer to the application site. However, striking a balance between system energy-efficiency and video quality satisfaction still remains a challenge. In this paper, we propose an edge-driven unified resource broker (URB) framework, viz., EdgeURB that seeks to find a trade-off between edge devices’ energy-efficiency and video configuration adaptation, with an aim to satisfying the real-time latency requirements without compromising analytics accuracy. Particularly, we design a two-stage algorithm: 1) a centralized algorithm for resource allocation, frame resolution selection, and user device to sever assignment and 2) a strategic game to further improve the energy-efficiency. We evaluate the performance of EdgeURB framework using an edge hardware testbed prototype that demonstrates EdgeURB’s success in simultaneously satisfying application latency, analytics accuracy, and devices’ energy consumption requirements. Also, through extensive simulations, we demonstrate EdgeURB’s schedulability and scalability improvement over baseline algorithms for a large number of devices and for varying edge resource availability.