Year: 2025
Type: conference-paper
Abstract: We present a novel scheduling framework for energy-efficient video analytics at the edge. The proposed approach dynamically adjusts GPU frequency and batch size to optimize inference execution while ensuring QoS compliance. It integrates a priority queue for QoS-aware scheduling and leverages offline profiling and ML-based estimation to select energy-optimal configurations. Based on these configurations, it employs a priority-based scheduler with greedy batching and adaptive waiting strategies to efficiently dispatch inference requests. Experimental evaluation conducted on an NVIDIA Jetson Xavier AGX device demonstrates a ~ 28% improvement in frames served per joule compared to a baseline with no scheduler. This outperforms a static batch scheduler (32 frames per batch) by ~ 15% and a time-triggered scheduler (batch formation every 200 ms) by ~ 29%, while introducing minimal increase in QoS violations (5%) relative to the best-performing policy.
Institutions: University of Patras, National Technical University of Athens
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4409727512