Content popularity prediction method based on depth learning under SDN architecture

Abstract

The invention discloses a content popularity prediction method based on depth learning under SDN architecture. The method comprises the steps that each node calculation resource and link in an SDN network to construct a reconfigurable and distributed depth learning network; the SDN network has an SDN controller and a number of SDN switch nodes; each SDN switch realizes the calculation function of a number of neurons; the neurons are connected with each other through the SDN switch link; each node in the SDN network collects space-time co-distribution data with requested content in real time and uses the data as the input of the depth learning network; a stack-type self-coder is used to carry out characteristic learning on the space-time co-distribution data; and a Softmax classifier is used to predict the content popularity. According to the invention, the reconfiguration of the depth learning network is realized based on the characteristics of programmable and global view centralized control SDN; the number of hidden layers and the nodes of each layer of neuron can be adjusted; and the method is very critical to an ICN analysis system, and can help dynamic routing and caching decisions in the system.

Description

technical field [0001] The invention relates to a method for predicting content popularity, in particular to a method for predicting content popularity based on deep learning under the SDN architecture. It belongs to the field of software-defined network and deep learning technology. Background technique [0002] At present, the research on content popularity mainly focuses on the popularity prediction of topics in social networks such as Weibo / Twitter. Content popularity can be measured from the perspective of space (communication range) or from the perspective of time (communication cycle). From the model point of view, at present, the research methods of microblog information popularity prediction are mainly based on the prediction method of infectious disease model and classification or regression model. For the infectious disease model, it originated from the early information diffusion theory, which mainly includes herd effect, information cascade, innovation diffusi...

AI Technical Summary

Problems solved by technology

[0003] However, the data that the current method relies on is not a global view, nor can it capture the spatial-temporal joint distribution characteristics of the predicted target object, so the prediction accuracy is not ideal

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