Two-Level Scheduling Algorithms for Deep Neural Network Inference in Vehicular Networks

In vehicular networks, task scheduling at the microarchitecture-level and network-level offers tremendous potential to improve the quality of computing services for deep neural network (DNN) inference. However, existing task scheduling works only focus on either one of the two levels, which results in inefficient utilization of computing resources. This paper aims to fill this gap by formulating a two-level scheduling problem for DNN inference tasks in a vehicular network, with an objective of minimizing total weighted sum of response time and energy consumption for all tasks under the following constraints: per task response time, per vehicle energy consumption, per vehicle storage capacity. We first formulate the problem and prove that it is NP-hard. A group transformation based algorithm, called GTA, is proposed. GTA makes scheduling decisions at the network-level using the group transformation based approach, and at the microarchitecture-level using a greedy strategy. In addition, an algorithm, denoted as DRL, is proposed to decrease total weighted sum of response time and energy consumption for all tasks. DRL trains two models with deep reinforcement learning to achieve two-level scheduling. The proposed algorithms are evaluated on a platform consisting of a desktop, Raspberry Pi, Eyeriss, OSM, SUMO, NS-3. Simulation results show that DRL outperforms the state-of-the-art methods for all cases, while the proposed GTA outperforms the state-of-the-art methods for most cases, in terms of total weighted sum of response time and energy consumption. Compared with four baseline algorithms, GTA and DRL reduce the total weighted sum of response time and energy consumption by 41.49% and 62.38%, on average respectively, for different numbers of tasks.