Joint caching and computing resource allocation for task offloading in vehicular networks

Zhi Wang; Ronghui Hou

Joint caching and computing resource allocation for task offloading in vehicular networks

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Author(s): Zhi Wang^{1, 2, 3} and Ronghui Hou^{1, 2}
- Affiliations: 1: School of Cyber Engineering, Xidian University , Xi'an , People's Republic of China ;
  2: State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences , Beijing , People's Republic of China ;
  3: Xiaomi Communications Co., Ltd , Beijing , People's Republic of China
Source: Volume 14, Issue 21, 22 December 2020, p. 3820 – 3827
DOI: 10.1049/iet-com.2020.0100 , Print ISSN 1751-8628, Online ISSN 1751-8636

Received 02/02/2020, Accepted 06/10/2020, Revised 06/07/2020, Published 25/11/2020

To meet the requirement of constrained delay and computation resource of the future vehicular networks, it is imperative to develop efficient content caching strategy and computation resource allocation strategy in mobile edge computing (MEC) servers. In the proposed network framework, since the caching capacity and computing resource of each MEC are limited, and the coverage areas of MECs are overlapped, the vehicular networks have to decide what contents to cache, how to offload tasks and how much computing resource needs to be allocated for each task. In this study, in order to jointly tackle these issues, we formulate caching strategy, offloading decision and computing resource allocation coordinately as a mixed integer non-linear programming (MINLP) problem. To solve the MINLP problem, we divide it into two subproblems. Firstly, we investigate a balanced and efficient caching strategy based on similarity in vehicular networks. Secondly, we apply McCormick Envelopes to convert MINLP problem into LP problem, and then adopt improved branch and bound algorithm to obtain the optimal offloading decision and computing resource allocation strategy. Simulation results indicate that the proposed schemes have a good performance in reducing economic cost under the deadline of each task.

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