基于神经网络流量预测的节能GPON

Aditi Phophaliya, Shalini Khare, A. Garg, V. Janyani
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引用次数: 0

摘要

随着第四次工业革命的到来,对高数据共享和数据存储的需求不断增加,导致对带宽的需求不断增加。此外,它还导致了能源消耗的大幅增加。虽然光网络的功率要求较低,但逐渐向全光网络转变,很快各地对光网络的高需求将导致巨大的总消耗。所提出的架构不仅支持日益增长的带宽需求,而且通过使用人工神经网络使其节能。人工神经网络算法用于预测网络的流量,从而智能地开关传输模块,以节省功耗。流量预测依赖于接入网,因此可以适应不同环境条件的变化模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy Efficient GPON Using Neural Network Traffic Prediction
Since the fourth industrial revolution has geared up, the demand for high data sharing and data storage has increased, which has led to the need for incremental bandwidth demand. Also, it has led to a massive increase in energy consumption. Although the power requirement of the optical network is low, gradually the trend is shifting to all-optical networks, and very soon the high demand for optical networks everywhere will lead to book significant overall consumption. The proposed architecture not only supports the growing demand for bandwidth but also makes it energy efficient by using artificial neural networks. ANN algorithms are used to predict the traffic flow of a network and hence smartly switch the transmitting module ON and OFF in order to save power consumption. Traffic prediction is access network dependent and therefore can adapt to the changing pattern of different environmental conditions.
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