Joint Service Caching and Computation Offloading Scheme Based on Deep Reinforcement Learning in Vehicular Edge Computing Systems
Vehicular edge computing (VEC) is a new computing paradigm that enhances vehicular performance by introducing both computation offloading and service caching, to resource-constrained vehicles and ubiquitous edge servers. Recent developments of autonomous vehicles enable a variety of applications that demand high computing resources and low latency, such as automatic driving, auto navigation, etc. However, the highly dynamic topology of vehicular networks and limited caching space at resource-constrained edge servers calls for intelligent design of caching placement and computation offloading. Meanwhile, service caching decisions are highly correlated to the computation offloading decisions, which pose a great challenge to effectively design service caching and computation offloading strategies. In this paper, the authors investigate a joint optimization problem by integrating service caching and computation offloading in a general VEC scenario with time-varying task requests. To minimize the average task processing delay, the authors formulate the problem using long-term mixed integer non-linear programming (MINLP) and propose an algorithm based on deep reinforcement learning to obtain a suboptimal solution with low computation complexity. The simulation results demonstrate that the authors' proposed scheme exhibits an effective performance improvement in task processing delay compared with other representative benchmark methods.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
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Supplemental Notes:
- Copyright © 2023, IEEE.
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Authors:
- Xue, Zheng
- Liu, Chang
- Liao, Canliang
- Han, Guojun
- Sheng, Zhengguo
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0000-0003-2143-4003
- Publication Date: 2023-5
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 6709-6722
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Serial:
- IEEE Transactions on Vehicular Technology
- Volume: 72
- Issue Number: 5
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 0018-9545
- Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Machine learning; Mobile computing; Resource allocation; Task analysis
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01882971
- Record Type: Publication
- Files: TRIS
- Created Date: May 23 2023 10:09AM