Title:Computing Task Offloading in Vehicular Edge Network via Deep
Reinforcement Learning
Volume: 19
Issue: 5
Author(s): Beibei He, Shengchao Su*Yiwang Wang
Affiliation:
- School of Electric and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
- Suzhou Key Laboratory of Smart Energy Technology, Suzhou, Jiangsu 215104, China
Keywords:
Vehicular edge network, edge computing, task offloading, task scheduling, priority, deep reinforcement learning.
Abstract:
Background: In recent years, with the development of the Internet of Vehicles, a variety
of novel in-vehicle application devices have surfaced, exhibiting increasingly stringent requirements
for time delay. Vehicular edge networks (VEN) can fully use network edge devices, such as roadside
units (RSUs), for collaborative processing, which can effectively reduce latency.
Objective: Most extant studies, including patents, assume that RSU has sufficient computing resources
to provide unlimited services. But in fact, its computing resources will be limited with the
increase in processing tasks, which will restrict the delay-sensitive vehicular applications. To solve
this problem, a vehicle-to-vehicle computing task offloading method based on deep reinforcement
learning is proposed in this paper, which fully considers the remaining available computational resources
of neighboring vehicles to minimize the total task processing latency and enhance the offloading
success rate.
Methods: In the multi-service vehicle scenario, the analytic hierarchy process (AHP) was first used
to prioritize the computing tasks of user vehicles. Next, an improved sequence-to-sequence
(Seq2Seq) computing task scheduling model combined with an attention mechanism was designed,
and the model was trained by an actor-critic (AC) reinforcement learning algorithm with the optimization
goal of reducing the processing delay of computing tasks and improving the success rate of
offloading. A task offloading strategy optimization model based on AHP-AC was obtained on this
basis.
Results: The average latency and execution success rate are used as performance metrics to compare
the proposed method with three other task offloading methods: Only-local processing, greedy strategy-
based algorithm, and random algorithm. In addition, experimental validation in terms of CPU
frequency and the number of SVs is carried out to demonstrate the excellent generalization ability of
the proposed method.
Conclusion: The simulation results reveal that the proposed method outperforms other methods in
reducing the processing delay of tasks and improving the success rate of task offloading, which
solves the problem of limited execution of delay-sensitive tasks caused by insufficient computational
resources.
