The heterogeneous network is the foundation of next-generation networks. It aims to explore the existing network resources effectively, and providing better QoS for every kind of traffic flow as far as possible. However, the diversity and dynamic nature of heterogeneous networks will bring a huge burden and big data to the network traffic control. Therefore, how to achieve efficient and intelligent network traffic control becomes the key problem of heterogeneous networks. In this article, an AI-inspired traffic control scheme is proposed. In order to realize fine-grained traffic control in heterogeneous networks, multi-dimensional (i.e., inter-layer, intra-layer, and caching and pushing) network traffic control is introduced. It is worth noting that backpropagation in deep recurrent neural networks is applied in the intra-layer such that an intelligent traffic control scheme can be derived efficiently when facing the huge traffic load in heterogeneous networks. Moreover, DBSCAN is adopted in the inter-layer, which supports efficient classification in the inter-layer. In addition, caching and pushing is adopted to make full use of network resources and provide better QoS. Simulation results demonstrate the effectiveness and practicability of the proposed scheme.

中文翻译：

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人工智能启发异构网络多维流量控制


异构网络是下一代网络的基础。其目的是有效地开发现有的网络资源，并尽可能为各种流量提供更好的服务质量。然而异构网络的多样性和动态性会给网络流量控制带来巨大的负担和大数据。因此，如何实现高效、智能的网络流量控制成为异构网络的关键问题。在本文中，提出了一种受人工智能启发的交通控制方案。为了实现异构网络中的细粒度流量控制，引入了多维度（即层间、层内、缓存和推送）网络流量控制。值得注意的是，深度循环神经网络中的反向传播应用于层内，使得在面对异构网络中的巨大流量负载时可以有效地导出智能流量控制方案。而且层间采用DBSCAN，支持层间高效分类。另外，采用缓存和推送的方式，充分利用网络资源，提供更好的QoS。仿真结果验证了该方案的有效性和实用性。