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Distributed Slice Selection-Based Computation Offloading for Intelligent Vehicular Networks
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DOI：10.1109/OJVT.2021.3087355.png)
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Abstract
En 中文
Distributed artificial intelligence (AI) is becoming an efficient approach to fulfill the high and diverse requirements for future vehicular networks. However, distributed intelligence tasks generated by vehicles often require diverse resources. A customized resource provision scheme is required to improve the utilization of multi-dimensional resources. In this work, a slice selection-based online offloading (SSOO) algorithm is proposed for distributed intelligence in future vehicular networks. First, the response time and energy consumption are reduced for processing tasks locally on the vehicles. Then, the offloading overheads, including latency and energy consumption, are calculated by considering the available resource amount, wireless channel states and vehicle conditions. The slice selection results is obtained by the deep reinforcement learning (DRL)-based method. Based on the selection solution, resource allocation results are achieved by KKT conditions and bisection method. Finally, the experimental results depict that the proposed SSOO algorithm outperforms other comparing algorithms in terms of energy consumption and task completion rate.
Keywords:
Task analysis
Servers
Artificial intelligence
Wireless communication
Delays
Resource management
Energy consumption
Resource slice
slice selection
computation offloading
distributed intelligence
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