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A Fast-adaptive Edge Resource Allocation Strategy for Dynamic Vehicular Networks

With the rapid development of vehicular networks, there is an increasing demand for extensive networking, computing and caching resources. In fact, vehicular networks are nonstationary, and how to allocate multiple resources effectively and efficiently for dynamic vehicular networks is extremely important, however, really challenging. In this paper, we propose a general framework that can enable fast-adaptive edge resource allocation for dynamic vehicular environment. Specifically, we model the dynamics of the vehicular environment as a series of related Markov Decision Processes (MDPs). We combine hierarchical reinforcement learning with meta learning, which makes our proposed framework available to quickly adapt to a new environment by only fine-tuning the top-level master network, and meanwhile the low-level sub-networks can make the right resource allocation policy. The extensive simulation results show the effectiveness of our proposed framework, which can quickly adapt to different scenarios. This is consistent with the real-world situations and can significantly improve the performance of resource allocation in dynamic vehicular networks.