Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
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DOI：
10.3390/app9183809
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Masaya Murakami;D. Kominami;K. Leibnitz;M. Murata
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文献类型：
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作者：
Masaya Murakami;D. Kominami;K. Leibnitz;M. Murata
在实现物联网假设的网络环境中，网络切片作为一种提高物理网络利用率的方法受到了广泛的关注。同时，切片已被证明通过将流量波动从一个网络传播到另一个网络而导致切片虚拟网络（VNs）之间的相互依赖。然而，对于相互依赖的互联网络，即网络中的网络（NoN），寻找一种可靠的、能够应对环境变化的设计方法是一个重要的问题，目前还有待解决。一些描述复杂系统中相互依赖网络行为的NoN模型已经存在，以往的研究表明，基于大脑功能网络的NoN模型可以获得较高的鲁棒性，但其在动态和实际系统中的应用尚未得到考虑。因此，本文提出了假设网络切片环境的物理-虚拟非（PV-NoN）模型。该模型定义了一个非可用状态来处理流量波动和PN与vpn之间的相互依赖。此外，我们假设该模型的虚拟网络之间存在三种基本的相互依赖类型。仿真实验证实，利用脑功能网络启发的互补相互依赖实现了高可用性和通信性能，同时防止了虚拟网络之间的干扰。研究了PV-NoN模型的可靠网络结构设计方法。为此，网络影响者（即整个网络中最具影响力的元素）的部署从网络内/网络间选型的角度进行配置。仿真实验证实，当每个虚拟网络分别以分类方式或非分类方式形成时，可用性或通信性能都有所提高。在网络间配性方面，当影响者在网络间非配性部署时，可用性和通信性能都得到了提高。
In realizing the network environment assumed by the Internet-of-Things, network slicing has drawn considerable attention as a way to enhance the utilization of physical networks (PNs). Meanwhile, slicing has been shown to cause interdependence among sliced virtual networks (VNs) by propagating traffic fluctuations from one network to others. However, for interconnected networks with mutual dependencies, known as a network of networks (NoN), finding a reliable design method that can cope with environmental changes is an important issue that is yet to be addressed. Some NoN models exist that describe the behavior of interdependent networks in complex systems, and previous studies have shown that an NoN model based on the functional networks of the brain can achieve high robustness, but its application to dynamic and practical systems is yet to be considered. Consequently, this paper proposes the Physical–Virtual NoN (PV-NoN) model assuming a network-slicing environment. This model defines an NoN availability state to deal with traffic fluctuations and interdependence among a PN and VNs. Further, we assume three basic types of interdependence among VNs for this model. Simulation experiments confirm that the one applying complementary interdependence inspired by brain functional networks achieves high availability and communication performance while preventing interference among the VNs. Also investigated is a method for designing a reliable network structure for the PV-NoN model. To this end, the deployment of network influencers (i.e., the most influential elements over the entire network) is configured from the perspective of intra/internetwork assortativity. Simulation experiments confirm that availability or communication performance is improved when each VN is formed assortatively or disassortatively, respectively. Regarding internetwork assortativity, both the availability and communication performance are improved when the influencers are deployed disassortatively among the VNs.