- Title
- Deep reinforcement learning-based multi-objective edge server placement in Internet of Vehicles
- Creator
- Lu, Jiawei; Jiang, Jielin; Balasubramanian, Venki; Khosravi, Mohammad; Xu, Xiaolong
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/188572
- Identifier
- vital:17317
- Identifier
-
https://doi.org/10.1016/j.comcom.2022.02.011
- Identifier
- ISSN:0140-3664 (ISSN)
- Abstract
- In the typical scenario of the Internet of Vehicles (IoV), the edge servers (ESs) are laid out near the road side units (RSUs) to process the collected data for a variety of IoV services in real time. Generally, because ESs are lightweight compared with cloud servers, if the ESs are not appropriately distributed, it will cause the unbalanced workload of the ESs. Thus, developing an ES plan to avoid the risk of overload and improve the quality of service (QoS) remains a challenge. To tackle it, a deep reinforcement learning-based multi-objective edge server placement strategy, named DESP, is fully explored, to promote the coverage rate, the workload balancing and reduce the average delay of finishing tasks in the IoV. In particular, the Markov Decision Process (MDP) of the ES placement problem is formulated and the deep reinforcement learning, i.e., Deep Q-Network (DQN) is applied to obtain the optimal placement scheme achieving the multiple objectives above. At last, a real vehicular data set is used for assessing the validity of DESP. © 2022 Elsevier B.V.
- Publisher
- Elsevier B.V.
- Relation
- Computer Communications Vol. 187, no. (2022), p. 172-180
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022 Elsevier B.V.
- Subject
- 4006 Communications engineering; 4009 Electronics, sensors and digital hardware; 4606 Distributed computing and systems software; Deep reinforcement learning; DQN; Edge server placement; IoV
- Reviewed
- Funder
- This research is supported by the Natural Science Foundation of Jiangsu Province of China under grant no. BK20211284 , and the Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps, China under grant no. 2020DB005 .
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