期刊
IEEE INTERNET OF THINGS JOURNAL
卷 9, 期 13, 页码 10397-10410出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3121796
关键词
Task analysis; Resource management; Energy consumption; Servers; Computational modeling; Vehicle dynamics; Internet of Things; Computation resource allocation; mobility; task offloading; vehicular edge computing (VEC)
资金
- Key-Area Research and Development Program of Guangdong Province [2020B010164002]
This article discusses the problem of computation efficiency in vehicular edge computing (VEC). By optimizing task offloading decisions and computation resource allocation, a mobility-aware computational efficiency-based task offloading and resource allocation (MACTER) scheme is proposed. Simulation results demonstrate that the proposed algorithm can efficiently enhance computation efficiency while satisfying computing time and energy consumption constraints.
Vehicular edge computing (VEC) is an innovative computing paradigm with an exceptional ability to improve the vehicles' capacity to manage computation-intensive applications with both low latency and energy consumption. Vehicles require to make task offloading decisions in dynamic network conditions to obtain maximum computation efficiency. In this article, we analyze computation efficiency in a VEC scenario, where a vehicle offloads its tasks to maximize computation efficiency as a tradeoff between computation time and energy consumption. Although, it is quite a challenge to ensure the quality of experience of the vehicle due to diverse task requirements and the dynamic wireless conditions caused by vehicle mobility. To tackle this problem, a computation efficiency problem is formulated by jointly optimizing task offloading decision and computation resource allocation. We propose a mobility-aware computational efficiency-based task offloading and resource allocation (MACTER) scheme and develop a distributed MACTER algorithm that provides the near-optimal solution. We further consider the fifth-generation new-radio vehicle-to-everything communication model, i.e., cellular link and millimeter wave, to enhance the system performance. The simulation outcomes demonstrate that the proposed algorithm can efficiently enhance computation efficiency while satisfying computing time and energy consumption constraints.
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