Revenue and Energy Efficiency-Driven Delay Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Ap

by   Xinyu Huang, et al.

For in-vehicle application,task type and vehicle state information, i.e., vehicle speed, bear a significant impact on the task delay requirement. However, the joint impact of task type and vehicle speed on the task delay constraint has not been studied, and this lack of study may cause a mismatch between the requirement of the task delay and allocated computation and wireless resources. In this paper, we propose a joint task type and vehicle speed-aware task offloading and resource allocation strategy to decrease the vehicl's energy cost for executing tasks and increase the revenue of the vehicle for processing tasks within the delay constraint. First, we establish the joint task type and vehicle speed-aware delay constraint model. Then, the delay, energy cost and revenue for task execution in the vehicular edge computing (VEC) server, local terminal and terminals of other vehicles are calculated. Based on the energy cost and revenue from task execution,the utility function of the vehicle is acquired. Next, we formulate a joint optimization of task offloading and resource allocation to maximize the utility level of the vehicles subject to the constraints of task delay, computation resources and wireless resources. To obtain a near-optimal solution of the formulated problem, a joint offloading and resource allocation based on the multi-agent deep deterministic policy gradient (JORA-MADDPG) algorithm is proposed to maximize the utility level of vehicles. Simulation results show that our algorithm can achieve superior performance in task completion delay, vehicles' energy cost and processing revenue.



There are no comments yet.


page 1

page 5

page 10


BARGAIN-MATCH: A Game Theoretical Approach for Resource Allocation and Task Offloading in Vehicular Edge Computing Networks

Vehicular edge computing (VEC) is emerging as a promising architecture o...

Vehicular Edge Computing via Deep Reinforcement Learning

The smart vehicles construct Vehicle of Internet which can execute vario...

Energy-Delay Minimization of Task Migration Based on Game Theory in MEC-assisted Vehicular Networks

Roadside units (RSUs), which have strong computing capability and are cl...

A Computation Offloading Incentive Mechanism with Delay and Cost Constraints under 5G Satellite-ground IoV architecture

The 5G Internet of Vehicles has become a new paradigm alongside the grow...

V2V-Based Task Offloading and Resource Allocation in Vehicular Edge Computing Networks

In the research and application of vehicle ad hoc networks (VANETs), it ...

Learn to Allocate Resources in Vehicular Networks

Resource allocation has a direct and profound impact on the performance ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.