Edge Service Migration for Vehicular Networks Based on Multi-agent Deep Reinforcement Learning
To meet the increasing resource demand of intelligent driving, roadside infrastructure is used to provide communication and computing capabilities to vehicles. Existing studies have leveraged deep reinforcement learning to perform small-scale resource scheduling for vehicles. It is critical to implement large-scale resource scheduling to deal with the high mobility of vehicles. However, this large-scale optimization is confronted with huge state and action space. To overcome this challenge, the authors propose an edge resource allocation method based on multi-agent deep reinforcement learning to reduce system cost while guarantee the quality of intelligent driving. The proposed method considers both immediate and long-term resource status, which helps to select appropriate base stations and edge servers. Trace driven simulations are performed to validate the efficiency of the proposed method.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9783030386504
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Supplemental Notes:
- © Springer Nature Switzerland AG 2020.
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Corporate Authors:
Springer International Publishing
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Authors:
- Zhang, Haohan
- Li, Jinglin
- Yuan, Quan
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Conference:
- 6th International Conference on Internet of Vehicles (IOV 2019)
- Location: Kaohsiung , Taiwan
- Date: 2019-11-18 to 2019-11-21
- Publication Date: 2020-1
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 344-352
- Monograph Title: Internet of Vehicles. Technologies and Services Toward Smart Cities: 6th International Conference, IOV 2019, Kaohsiung, Taiwan, November 18–21, 2019, Proceedings
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Serial:
- Lecture Notes in Computer Science
- Volume: 11894
- Publisher: Springer Cham
- ISSN: 0302-9743
- Serial URL: https://www.springer.com/series/558
Subject/Index Terms
- TRT Terms: Intelligent vehicles; Machine learning; Mobile communication systems; Resource allocation
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01892954
- Record Type: Publication
- ISBN: 9783030386504
- Files: TRIS
- Created Date: Sep 12 2023 1:41PM