Paper Info
Title
A collaborative computation and dependency-aware task offloading method for vehicular edge computing: a reinforcement learning approach
Abstract
Vehicular edge computing (VEC) is emerging as a new computing paradigm to improve the quality of vehicular services and enhance the capabilities of vehicles. It enables performing tasks with low latency by deploying computing and storage resources close to vehicles. However, the traditional task offloading schemes only focus on one-shot offloading, taking less into consideration task dependency. Furthermore, the continuous action space problem during task offloading should be considered. In this paper, an efficient dependency-aware task offloading scheme for VEC with vehicle-edge-cloud collaborative computation is proposed, where subtasks can be processed locally or can be offloaded to an edge server, or a cloud server for execution. Specifically, first, the directed acyclic graph (DAG) is utilized to model the dependency of subtasks. Second, a task offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) was proposed to obtain the optimal offloading strategy in a vehicle-edge-cloud environment, which efficiently solves the continuous control problem and helps reach fast convergence. Finally, extensive simulation experiments have been conducted, and the experimental results show that the proposed scheme can improve performance by about 13.62% on average against three baselines.
Year
DOI
Venue
2022
10.1186/s13677-022-00340-3
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
Keywords
DocType
Volume
Task offloading, Task dependency, Vehicular edge computing, Vehicle-edge-cloud collaborative computing, Deep deterministic policy gradient, Deep reinforcement learning
Journal
11
Issue
ISSN
Citations 
1
2192-113X
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Guozhi Liu100.34
Fei Dai2154.22
Bi Huang300.34
Zhenping Qiang400.34
Shuai Wang500.34
Lecheng Li600.34