基于边缘计算的智能电网弹性光网络流量识别

Zhiang Du, Xiukun Pang
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引用次数: 0

摘要

在弹性光网络中,业务感知是一个非常重要的问题,必须加以重视。针对这一问题,本文提出了一种边缘计算回声状态网络(E-ESN)模型,该模型通过简化油藏计算,在原有回声状态网络模型的基础上进行了较大改进。此外,为了满足多业务需求,还提出了业务流量识别算法。为此,设计了基于E-ESN的软件定义光网络业务感知算法,并给出了实现方法。仿真结果表明,该改进方案能够适应弹性光网络,具有较好的多业务支持能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Edge Computing Based Traffic Identification in Elastic Optical Networks for Smart Power Grid
The service awareness is of great importance and should be emphasized in Elastic Optical Networks (EON). Aimed to solve this problem, this article proposes an edge computing echo-state-network (E-ESN) model, which is improved greatly from original echo-state-network model by simplifying the reservoir computation. Moreover, the service traffic identification algorithm is also presented to match the multi-service demand. Thus, this proposed E-ESN based service awareness algorithm of software defined optical network is designed, together with the implement approach. Simulation results show that this proposed enhanced scheme can suite for the elastic optical network with better multi-service support ability.
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