Hybrid Resource Scheduling Scheme for Video Surveillance in GPU-FPGA Accelerated Edge Computing System

Video surveillance system with object re-identification is cited as a challenge to address to enhance the safety and convenience of citizens. The system consists of a combination of complex tasks requiring a lot of computing workload. With these characteristics, efforts have continued to accelerate the system. Existing systems did not benefit from the service latency perspective to make good use of heterogeneous accelerated edge computing system. In this paper, the goal is to accelerate the system used in smart cities on limited heterogeneous edge servers, and the scheduling planning method considering them is proposed. We first identify the computational volume-based execution time model of the heterogeneous accelerators. Then, we propose a scheduling plan that distributes this task graph to resources. Finally, the planning method proposed in this paper is experimentally compared with the previous GPU-FPGA allocation scheme. We compare it to the previously proposed method, and show that queue latency can be reduced, with showing robustness to the deadline violation rate.