QoS Guaranteed Network Slicing Orchestration for Internet of Vehicles
QoS Guaranteed Network Slicing Orchestration for Internet of Vehicles
复制标题
QoS保证的车联网网络切片编排
DOI：
10.1109/jiot.2022.3147897
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发表时间：
2022-08
影响因子：
10.6
通讯作者：
Wang Ruyan
中科院分区：
文献类型：
--
作者：
Cui Yaping;Huang Xinyun;He Peng;Wu Dapeng;Wang Ruyan
为了满足不同应用场景对服务质量（QoS）的需求，移动蜂窝网络中引入了网络切片技术。它允许移动蜂窝网络运营商根据指定需求灵活地在公共网络基础设施上完成多个逻辑隔离网络的创建。同时，在车联网（IoV）中，为车辆提供稳定的QoS是一个非常棘手的问题，特别是在动态的车辆环境中。因此，本文研究了车联网切片问题，提出了一种保证QoS的网络切片编排，即基于长短期记忆的深度确定性策略梯度算法（LSTM-DDPG），以保证切片的稳定性能。具体来说，我们首先将资源分配问题解耦为两个子问题。然后，利用深度学习和强化学习（RL）协同分配资源来解决这两个问题。利用深度学习LSTM跟踪车辆环境长期变化特征，利用RL算法DDPG进行在线资源调优。大量的仿真结果证明了LSTM-DDPG算法的有效性，该算法能够以大于92%的概率为车辆提供稳定的QoS。我们还演示了所提出的编排对不同切片环境的适应性，并且与其他算法相比，性能始终是最佳的。
To support the diversified Quality of Service (QoS) requirements of application scenarios, network slicing has been introduced in the mobile cellular network. It allows mobile cellular network operators to accomplish the creation of multiple logically isolated networks on common network infrastructure flexibly depending on specified demands. Meanwhile, in Internet of Vehicles (IoV), it is very intractable to supply a stable QoS for the vehicles, especially for the dynamic vehicular environments. Thus, we investigate the IoV slicing problem in this article, and propose a QoS guaranteed network slicing orchestration, namely, the long short-term memory-based deep deterministic policy gradient algorithm (LSTM-DDPG), to ensure the stable performance for the slices. Specifically, we first decouple the resource allocation problem into two subproblems. After that, the deep learning and reinforcement learning (RL) are used to allocate resources collaboratively to solve these two questions. We use deep learning LSTM to track the characteristic of the long-term vehicular environment changing, and the RL algorithm DDPG is utilized for online resource tuning. Extensive simulations have proved the effectiveness of the LSTM-DDPG, which can offer stable QoS to the vehicles with a probability greater than 92%. We also demonstrated the adaptiveness of the proposed orchestration with different slicing environments, and the performance is always optimal compared to that of other algorithms.