Incentive-Based Two-Level Scheduling Algorithms for Load Balance in Vehicular Edge Computing

Abstract

In vehicular edge computing (VEC), two-level scheduling both at intravehicle and intervehicle offers great potential to improve Quality of Services (QoSs) for deep neural network (DNN) inference. However, existing works on two-level scheduling failed to jointly consider load balance among vehicles and the selfishness of vehicles, which results in the absence of guarantee in QoSs. This article seeks to fill this gap by formulating an incentive problem associated with two-level scheduling aimed at load balance for DNN inference in VEC, with the objective of maximize the system utility in VEC under the constraints of per task response time, per vehicle energy consumption, per vehicle utility guarantee, etc. Then, we prove the problem is NP-complete. A coalition-based incentive algorithm, called CBA, is proposed. CBA makes intravehicle scheduling decisions by a heuristic strategy and it makes intervehicle scheduling decisions by a coalition game-based strategy. The Nash-stable and convergence for CBA are proved. In addition, a deep reinforcement learning-based algorithm, called DRL, is proposed to solve the formulated problem. DRL introduces a heuristic strategy to generate the intravehicle scheduling decisions, and it exploits deep reinforcement learning method to generate the intervehicle scheduling decisions. The proposed algorithms are evaluated on a platform with CPUs, SCALE-Sim, OSM and SUMO. Simulation results show that two proposed algorithms outperform the state-of-the-art methods for all cases, in terms of system utility. Compared with two baseline algorithms, CBA and DRL improve system utility by an average of $0.56\times $ and $1.24\times $ , respectively, for different numbers of vehicles.