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The Journal of Supercomputing

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06-05-2023

Computation offloading and heterogeneous task caching in MEC-enabled vehicular networks

Authors: Ruizhi Wu, Bo Li

Published in: The Journal of Supercomputing | Issue 15/2023

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Abstract

Given the rapid growth of business types and the characteristics of delay-sensitive tasks in vehicular networks, vehicles’ computing power is insufficient to handle the demands of a wide range of computing tasks. Considering the privacy of vehicles’ tasks in the future IoV, we categorize vehicular tasks into two groups, which is our definition about heterogeneous tasks. To fulfill the massive demands for processing and storage resources, more and more heterogeneous tasks are going to be processed in mobile edge computing (MEC) servers. In this paper, we have proposed a vehicular network architecture that allows for heterogeneous tasks to be processed on the roadside units co-located with MEC servers. Based on this architecture, taking into account communication, task requesting, computing, and caching, a mixed integer nonlinear programming problem is formulated to jointly optimize the task completion delay and energy consumption. To solve the problem, a grey wolf optimizer-joint optimization algorithm is designed to obtain a better offloading and caching decision. Experimental results show that this algorithm has significantly improved performance over other algorithms.

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Title: Computation offloading and heterogeneous task caching in MEC-enabled vehicular networks
Authors: Ruizhi Wu
Bo Li
Publication date: 06-05-2023
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 15/2023
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-023-05328-7

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