- Title
- Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System
- Creator
- Ning, Zhaolong; Dong, Peiran; Wang, Xiaojie; Rodrigues, Joel; Xia, Feng
- Date
- 2019
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/171434
- Identifier
- vital:14348
- Identifier
-
https://doi.org/10.1145/3317572
- Identifier
- ISBN:2157-6904
- Abstract
- The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.
- Publisher
- Association for Computing Machinery
- Relation
- ACM Transactions on Intelligent Systems and Technology Vol. 10, no. 6 (Dec 2019), p. 24
- Rights
- Copyright © 2020 ACM, Inc.
- Rights
- This metadata is freely available under a CCO license
- Rights
- Open Access
- Subject
- 0801 Artificial Intelligence and Image Processing; 0806 Information Systems; Nonorthogonal multiple-access; Networks; Allocation; Protocol; Utility
- Full Text
- Reviewed
- Hits: 1873
- Visitors: 3022
- Downloads: 1512
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Accepted | 1 MB | Adobe Acrobat PDF | View Details Download |