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Optimizing Task Offloading and Resource Allocation in Vehicular Edge Computing Based on Heterogeneous Cellular Networks
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DOI：10.1109/TVT.2023.3345364.png)
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摘要
En 中文
5G is a promising technology for improving the Quality of Service (QoS) in Internet of Vehicles (IoV) applications, including Vehicular Edge Computing (VEC). However, 5G networks have a limited communication range due to their radio-frequency properties, which can be a challenge in dynamic IoV environments. To address this issue, we propose a VEC architecture based on heterogeneous cellular networks, in which vehicles can select the appropriate communication network by classifying tasks according to their maximum tolerable latency. In order to further enhance the overall performance of the VEC system, we developed an efficient scheme that optimizes task offloading decisions and computation resource allocation in the proposed architecture. We analyze and formulate the optimization problem and use the linear relaxation improved branch-and-bound algorithm to solve it. Through extensive simulations, we demonstrate that the proposed scheme is superior to other solutions in computing latency, energy consumption, and failure rate.
Keyword:
Task analysis
5G mobile communication
Base stations
Computer architecture
Resource management
Energy consumption
Servers
Vehicular edge computing (VEC)
task offloading
computation resource allocation
task classification
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
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