Optical network routing optimization method based on LSTM deep learning and related device thereof

Abstract

One or more embodiments of the present specification provide an optical network routing optimization method based on LSTM deep learning and a related device thereof, which apply an LSTM prediction model in deep learning, and by learning a mapping relationship between link usage rates and service remaining time characteristics in different scenes and a complex routing strategy, link and service overall information in the network used as input, a reconstruction threshold is quickly obtained through calculation of several layers of neural networks to decide whether to perform a routing optimization strategy or not. According to the method, the defects of limited feature learning capability, poor expression capability for complex functions, single optimization target parameter, limited switchresources and huge network resource overhead of algorithms in the prior art are overcome, and meanwhile, the threshold value can be efficiently changed in real time along with the continuous arrival of services, therefore, the situation of service congestion caused by network flow fluctuation or excessive bearing service volume is relieved, and the adaptive routing optimization of the optical network service is realized.

Description

technical field [0001] One or more embodiments of this specification relate to the technical field of routing optimization, and in particular to a method for optimizing routing of an optical network based on LSTM deep learning and related devices. Background technique [0002] Traditional optical network routing optimization methods include rerouting and spectrum shifting. Rerouting means that some services on the non-optimal path are re-routed to select a new optimal path for transmission to improve the overall performance of the network. When the network carries services for transmission, many services are not allocated on the shortest or optimal path because the resource requirements of the services are not met. When the service request on the shortest path is removed, the service on other non-optimal paths can be transferred to the shortest or optimal path through rerouting, thereby optimizing the use of network resources and improving the overall performance of the net...

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Problems solved by technology

[0004] In view of this, the purpose of one or more embodiments of this specification is to propose an optical network routing optimization method based on LSTM deep learning and related devices to solve the problem of limited feature learning ability of algorithms in the prior art, and complex functions The problem of poor expression ability, single optimization target parameter, limited switch resources and huge network resource overhead

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