Telecommunication networks are evolving toward a data-center-based architecture, which includes physical network functions, virtual network functions, as well as various types of management and orchestration systems. The primary purpose of this type of heterogeneous network is to provide efficient and convenient communication services for users. However, the diverse factors of a heterogeneous network such as bandwidth, delay, and communication protocol, bring great challenges for routing recommendations. In addition, the growing volume of big data and the explosive deployment of heterogeneous networks have started a new era of applying big data technologies to implement routing recommendations. In this article, a tensor-based big-data-driven routing recommendation framework, including the edge plane, fog plane, cloud plane, and application plane, is proposed. In this framework, a tensor-based, holistic, hierarchical approach is introduced to generate efficient routing paths using tensor decomposition methods. Also, a tensor matching method including the controlling tensor, seed tensor, and orchestration tensor is employed to realize routing recommendation. Finally, a case study is used to demonstrate the key processing procedures of the proposed framework.

中文翻译：

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异构网络中基于张量的大数据驱动路由推荐方法

电信网络正在向基于数据中心的体系结构发展，该体系结构包括物理网络功能，虚拟网络功能以及各种类型的管理和编排系统。这种异构网络的主要目的是为用户提供高效便捷的通信服务。但是，异构网络的各种因素（例如带宽，延迟和通信协议）给路由建议带来了巨大挑战。此外，大数据量的增长和异构网络的爆炸性部署已经开始了一个应用大数据技术来实现路由建议的新时代。在本文中，基于张量的大数据驱动路由推荐框架，包括边缘平面，雾平面，云平面和应用程序平面，被提议。在此框架中，引入了基于张量的整体分层方法，以使用张量分解方法生成有效的路由路径。另外，采用包括控制张量，种子张量和编排张量的张量匹配方法来实现路由推荐。最后，通过案例研究来说明所提出框架的关键处理程序。