Open Software Stack for Compression-Aware Adaptive Edge Offloading

Abstract

Offloading a computationally complex task from an edge device can improve latency and its battery life. The additional network transfers increase power consumption and latency, but can be mitigated with compression at the cost of additional computation and distortion. Thus, a balance between compression efficiency and complexity must be found and maintained as the network conditions change. In this paper, we propose an open -source edge offloading software stack that decides between local and remote task execution and chooses the optimal compression method based on continuously monitored system metrics under user-defined constraints. We evaluate the system offloading a semantic segmentation task from a smartphone over WiFi-6 and 5G networks, using latency, intersection over union (IoU), and power consumption metrics. Portability, multi-tenancy, and granular profiling are achieved by leveraging the PoCL-R OpenCL implementation. In simulated network impairments, dynamically selecting the compression strategy achieves 2.1-10.7% average latency improvement but maintains the highest possible quality when network conditions allow meeting the latency budget. If the computational overhead of compression surpasses the network transfer overhead, the system can transmit images uncompressed. Field measurements under network impairments confirm the usability of the system and its ability to fall back to local execution.