ADPTD: Adaptive Data Partition With Unbiased Task Dispatching for Video Analytics at the Edge

Abstract

Recently, edge-assisted methods have been proposed as a promising technique to deliver fast and accurate on-device video analytics by partitioning frame data and dispatching them to edge servers for parallel execution. However, the data partition (DP) reduces the detection latency but decreases accuracy since objects may cross the boundaries of adjacent blocks. The effect of DP on the accuracy and latency depends on multiple vital parameters (e.g., target size, density, network, and computing resources) in an unknown and time-varying fashion. Moreover, these parameters are determined by the application scenarios and edge environment, which are uncertain and heterogeneous at the edge. Hence, how to partition frames to strike a balance between accuracy and latency is a nontrivial and intractable problem. To this end, we propose an online learning-based device-edge–cloud collaboration framework, ADPTD, to guide DP at the edge. We propose an optimal task dispatching algorithm (OTD) to minimize detection latency. Then, we propose a multiarmed bandit-based algorithm to pick a DP strategy and invoke OTD to dispatch tasks in each time slot. Theoretical analysis reveals that ADPTD achieves sublinear regret. Extensive experimental results show that ADPTD outperforms the state-of-the-art methods, achieving a latency reduction of up to $2.53\times $ and improving accuracy by up to 49.4%.