Dynamic resource allocation for jointing vehicle-edge deep neural network inference

Abstract

The emergence of mobile edge computing provides an efficient and stable computing platform for intelligent applications of autonomous vehicles, and deep neural network (DNN) based tasks collaborative inference through joint device-edge is considered an effective way to reduce latency. However, the computing resources allocated to the vehicle are dynamic as the number of requesters changes due to the limitation of edge server resources, which causes the best partition point of the DNN is not fixed. In this paper, we consider a dynamic resource allocation scheme to select the best partition point of DNN inference tasks by vehicle-edge collaborative computing. Specifically, the latency constrained DNN tasks of vehicles are partially offloaded to edge at the granularity of DNN layers. Considering the heterogeneity of vehicular computing capabilities and multiple DNN inference tasks, we formulate an optimization problem for dynamic resource allocation and automatically select the best partition point to minimize the overall latency of all vehicles, which is NP-hard. Then we design a chemical reaction optimization based algorithm for low complexity to solve the problem. The results of extensive evaluations illustrate that our proposed scheme is superior to other baseline schemes in terms of overall latency, and with lower failure rate.