In today’s time, there is an enormous amount of data in the cyber world. Optical network communication using machine learning is a technique that reduces the complexity of the architecture by using software Defined Network or simply SDN. SDN has the ability to for automation of network operations, including monitoring and troubleshooting. Moreover, it provides features like plug-n-play or zero-touch network operations. One of the most common usages of machine learning is for solving the classical Routing and Wavelength Assignment from previous data configuration.
In the proposed research of Optical network communication using machine learning, the foremost step is to gather good quality data and to create a code to understand the relation between the data variables.
Next up is building the architecture of the model by dividing it into various sets so as to prepare it for the process of fine tuning to optimally adjust its hyper-parameters which will lead to avoidance of over fitting and under fitting. The Deep Neural Network has been chosen for Optical network communication using machine learning that comprises of six linked layers, that consists of dropout in the first and fourth layer. This is also structured with 12 regularizations in the third and fifth layers.
Issues in the current technology
In the current technology, there is a Routing and wavelength Assignment problem and fiber access delay modeling. The proposed research on Optical network communication using machine learning is aimed to resolve both of these major shortcomings.
To understand the relation between the variables of the data a mathematical equation is proposed;
Here, represents the information from X to and represents the error which accounts for what X cannot explain about
Here the proposed solution forOptical network communication using machine learningthe network simulator is having 48,096 traffic matrices. The Deep Neural Network or DNN model configuration is selected that is trained offline with real data and then it resolves the Routing and wavelength Assignment problem in two order of magnitude with values lesser than a well trained Deep Neural Network.
In the future, the performance of the proposed model can be improved by analyzing the effect of provisioning period.
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