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首页> 外文期刊>International journal of applied mechanics >Spectrum trading between virtual optical networks with time-varying traffic in an elastic optical network
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Spectrum trading between virtual optical networks with time-varying traffic in an elastic optical network

机译:虚拟光网络之间的频谱交易在弹性光网络中具有时变流量的频谱交易

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摘要

Network virtualization can improve the utilization of a physical network by enabling the coexistence of multiple virtual networks supporting different services. For an elastic optical network, network virtualization would create a number of virtual optical networks (VONs) embedded in the same physical optical network. Conventionally, each of these VONs is allocated with a fixed amount of network resources. However, the traffic demand on a VON is usually dynamic, varying over time. This leads to a mismatch between the fixed resource assigned and the actual resource required by users. Therefore, some user services may be blocked and the quality of service provided by each VON may be degraded. To overcome the disadvantage caused by this mismatch, we propose a network resource-sharing scheme, called spectrum trading (ST), which trades spectrum resources between different VONs based on their actual capacity requirements, for better overall spectrum utilization. Integer linear programming (ILP) models are developed to model the different ST scenarios, and heuristic algorithms are developed to tackle ST in large networks. Simulation studies show that the proposed ST scheme is effective in significantly improving the total traffic demand carried by VONs by up to 20% in the test networks considered, while not bringing in any additional hardware cost. (C) 2020 Optical Society of America
机译:网络虚拟化可以通过支持支持不同服务的多个虚拟网络的共存来改进物理网络的利用。对于弹性光网络,网络虚拟化将创建嵌入在相同物理光网络中的许多虚拟光网络(VONS)。传统上,这些VONS中的每一个都以固定的网络资源分配。但是,von对von的交通需求通常是动态的,随着时间的推移而变化。这导致已分配的固定资源和用户所需的实际资源之间的不匹配。因此,可以阻止一些用户服务,并且每个von提供的服务质量可能会降低。为了克服这种不匹配引起的缺点,我们提出了一种网络资源共享方案,称为Spectrum Trading(ST),该方案基于其实际容量要求,在不同的vons之间交易频谱资源,以获得更好的整体频谱利用。 Integer线性编程(ILP)模型是开发的,以模拟不同的ST场景,并且开发出启发式算法以在大型网络中解决St。仿真研究表明,所提出的ST方案有效地在考虑的测试网络中显着提高了高达20%的vons持续的总交通需求,同时不会带来任何额外的硬件成本。 (c)2020美国光学学会

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