Dependence-Aware Multi-Task Scheduling for Edge Video Analytics With Accuracy Guarantee

In this paper, we investigate the optimal configuration and dependence-aware task assignment for multi-task edge video analytics. Multi-task video analytics involves multiple objects in video frames and multiple dependent tasks, resulting in existing video configuration and task assignment scheme for single-task unsuitable to this scenario. Our paper aims to efficiently assign dependent tasks to multiple collaborative edge nodes with appropriate video configuration, to achieve low latency while maintain accuracy. Firstly, we conduct extensive experiments on real-world video datasets. The results reveal that the impact of resolution on the detection accuracy varies among different sizes of objects. Moreover, the computing and communication load of dependent tasks varies along the time due to the dynamic video content. Based on the experimental results, we propose a threshold-based downsampling strategy for large objects, aiming at minimizing the transmission latency while guaranteeing task analytic accuracy. In addition, the number of objects and workload of subsequent tasks turn out to be highly correlated, the computation and transmission demands of tasks can be thus estimated for each video chunk. Then, a heuristic dependence-aware task assignment algorithm is proposed to achieve minimum completion time of dependent tasks. Experimental results demonstrate that the proposed scheme can effectively reduce the execution time of multiple tasks while guaranteeing the analytic accuracy, outperforming the state-of-the-art benchmarks.