Computational Offloading for Real-Time Computer Vision in Unreliable Multi-Tenant Edge Systems

The demand and interest in serving Computer Vision applications at the Edge, where Edge Devices generate vast quantities of data, clashes with the reality that many Devices are largely unable to process their data in real time. While computational offloading, not to the Cloud but to nearby Edge Nodes, offers convenient acceleration for these applications, such systems are not without their constraints. As Edge networks may be unreliable or wireless, offloading quality is sensitive to communication bottlenecks. Unlike seemingly unlimited Cloud resources, an Edge Node, serving multiple clients, may incur delays due to resource contention. This project describes relevant Computer Vision workloads and how an effective offloading framework must adapt to the constraints that impact the Quality of Service yet have not been effectively nor properly addressed by previous literature. We design an offloading controller, based on closed-loop control theory, that enables Devices to maximize their throughput by appropriately offloading under variable conditions. This approach ensures a Device can utilize the maximum available offloading bandwidth. Finally, we constructed a realistic testbed and conducted measurements to demonstrate the superiority of our offloading controller over previous techniques.