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Virtual network mapping method and model training method and device thereof

A technology of virtual network mapping and model training, which is applied in the field of devices, virtual network mapping methods and model training methods, can solve the problems of affecting the virtual network mapping effect of the model, low efficiency, gradient disappearance, etc., and achieve the effect of improving the mapping effect

Active Publication Date: 2020-03-17
BEIJING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

However, the existing model can only process serially when extracting the information of each node in the physical network, and the efficiency is low
At the same time, due to too many neurons in the existing model, gradient disappearance or gradient explosion is prone to occur, which affects the final virtual network mapping effect of the model.

Method used

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  • Virtual network mapping method and model training method and device thereof
  • Virtual network mapping method and model training method and device thereof
  • Virtual network mapping method and model training method and device thereof

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Embodiment Construction

[0030] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. the embodiment. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0031] With the continuous development of machine learning and deep reinforcement learning, more and more artificial intelligence algorithms have been introduced into network virtualization to solve the virtual network mapping problem. However, in the existing process of using artificial intelligence-related algorithms to solve virtual network mapping, data can often only be processe...

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Abstract

The invention provides a virtual network mapping method and a model training method and device thereof, and the model training method comprises the steps: obtaining a training set according to the node information in a physical network, preprocessing the training set, and obtaining index information of the node information in the training set; inputting a preprocessed training set into a coding unit of a model for training to obtain a first training result; inputting the first training result into a decoding unit of the model for training to obtain a second training result; calculating a lossvalue of the model by using a preset loss function according to the second training result; updating model parameters through the loss value; judging whether the loss value meets a preset loss threshold value or not; if yes, training is ended, and a model used for virtual network mapping is obtained. Through the plurality of encoders in the encoding unit and the plurality of decoders in the decoding unit and by adopting a reinforcement learning algorithm for training, the mapping effect of the model on the virtual network is improved.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a virtual network mapping method and its model training method and device. Background technique [0002] Network virtualization is a technology that simulates multiple logical networks in a physical network. With the development of artificial intelligence technology, algorithms in the field of machine learning can be used to realize network virtualization, thereby solving the problem of virtual network mapping. For example, RNN (Recurrent Neural Network, cyclic neural network) is used to extract physical network information, and sequentially output mapping results, which can simulate the mapping process of the actual virtual network. However, the existing models can only process serially when extracting the information of each node in the physical network, and the efficiency is low. At the same time, due to too many neurons in the existing model, gradient disappearance or ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24G06N3/08
CPCG06N3/084H04L41/145
Inventor 姚海鹏张培颖马思涵纪哲
Owner BEIJING UNIV OF POSTS & TELECOMM
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