Paper Info
Title
A Joint Service Migration and Mobility Optimization Approach for Vehicular Edge Computing
Abstract
The vehicular edge computing is considered an enabling technology for intelligent and connected vehicles since the optimization of communication and computing on edge has a significant impact on driving safety and efficiency. In this paper, with the road traffic assignment to “proactively” reshape the spatiotemporal distribution of resource demands, we investigate the joint service migration and mobility optimization problem for vehicular edge computing. The goal is to meet the service delay requirements of vehicular edge computing with minimum migration cost and travel time. As service migration and mobility optimization are coupled, the joint scheduling problem suffers from the curse of dimensionality, which cannot be solved in real time by centralized algorithms. To this end, a multi-agent deep reinforcement learning (MADRL) algorithm is proposed to maximize the composite utility of communication, computing, and route planning in a distributed way. In the MADRL algorithm, a two-branch convolution based deep Q-network is constructed to coordinate migration action and routing action. Extensive experimental results show that the proposed algorithm is scalable and substantially reduces service delay, migration cost and travel time as compared with the existing baselines.
Year
DOI
Venue
2020
10.1109/TVT.2020.2999617
IEEE Transactions on Vehicular Technology
Keywords
DocType
Volume
Vehicular edge computing,service migration,mobility optimization,multi-agent deep reinforcement learning
Journal
69
Issue
ISSN
Citations 
8
0018-9545
8
PageRank 
References 
Authors
0.46
0
6
Name
Order
Citations
PageRank
Quan Yuan15511.07
Jinglin Li215030.39
Haibo Zhou322522.71
Tao Lin4120.83
Guiyang Luo5234.35
Xuemin Shen615389928.67