Towards Hit-Interruption Tradeoff in Vehicular Edge Caching: Algorithm and Analysis

Recent advancements in edge computing and edge caching provide a feasible solution to support a plethora of new applications such as on-demand videos, AR/VR, road surveillance. However, to apply edge caching in vehicular scenarios is still difficult due to the unkonwn request pattern of vehicular users and intermittent service links between vehicles and edge servers (e.g., Road Side Units, RSUs). In this paper, the authors aim to investigate the vehicular edge caching problem in practical vehicular scenarios by considering higher hit ratio, while avoiding interruption of caching services. Specifically, to obtain a higher hit ratio, they firstly propose an on-demand adaptive cache algorithm. The algorithm can adjust the eviction time of cached contents by tracking the dynamics of requests and content popularity. They then develop an analysis framework to model the interruption performance of caching services from RSUs. Through diffraction approximation theory, the service process can be modeled as a joint process of the movement and stopping of vehicles to deduce the interruption ratio. To apply the on-demand adaptive cache algorithm in practical scenarios, the final caching decisions should be corrected by incorporating the interruption performance. Therefore, a <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-fair utility-oriented vehicular edge caching scheme is developed, which can achieve the tradeoff of hit ratio and interruption ratio. Performance evaluation shows the advantages of the authors' proposed vehicular caching scheme in hit ratio, accuracy of analysis model, utility, respectively.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2021.3052355
• Record URL:
  https://ieeexplore.ieee.org/document/9349200
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Zhang, Yao
  • Li, Changle
  • Luan, Tom H
  • Yuen, Chau
  • Fu, Yuchuan
  • Wang, Hui
  • Wu, Weigang
• Publication Date: 2022-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 5198-5210
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 6
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Cloud computing; Computer models; Mobile computing; Optimization; Vehicle dynamics
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01852073
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 21 2022 11:30AM