A Tensor-Based Big-Data-Driven Routing Recommendation Approach for Heterogeneous Networks
A Tensor-Based Big-Data-Driven Routing Recommendation Approach for Heterogeneous Networks
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DOI：
10.1109/mnet.2018.1800192
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发表时间：
2019-01-01
期刊：
影响因子：
9.3
通讯作者：
Deen, M. Jamal
中科院分区：
文献类型：
--
作者：
Wang, Xiaokang;Yang, Laurence T.;Deen, M. Jamal
电信网络正朝着基于数据中心的架构发展，该架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。这类异构网络的主要目的是为用户提供高效、便捷的通信服务。然而，异构网络中的带宽、时延、通信协议等多种因素给路由推荐带来了很大的挑战。此外，不断增长的大数据量和异构网络的爆炸式部署开启了应用大数据技术实现路由推荐的新时代。本文提出了一种基于张量的大数据驱动的路由推荐框架，包括边缘平面、雾平面、云平面和应用平面。在这个框架中，一个基于张量的，整体的，层次化的方法被引入到使用张量分解方法生成高效的路由路径。同时，采用控制张量、种子张量和编排张量相结合的张量匹配方法实现路由推荐。最后，通过一个案例研究来说明所提出的框架的关键处理过程。
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.