Virtual network function dynamic migration method based on deep belief network resource demand forecasting

Abstract

The invention relates to a virtual network function dynamic migration method based on deep belief network resource demand forecasting, and belongs to the field of mobile communication. The method comprises the following steps: (S1) in view of the dynamic features of SFC business resource demand in a slicing network, establishing a system overhead model of comprehensive migration overhead and bandwidth overhead; (S2) in order to realize spontaneous VNF migration, monitoring the resource utilization condition of virtual network function or link in real time, and discovering the deployed bottom nodes or resource hot spots in the link in time by using an online learning based adaptive DBN forecasting method; (S3) designing a topology awareness based dynamic migration method according to the forecasting result, so as to reduce system overhead; (S4) proposing a tabu search based optimization method to further optimize the migration strategy. The forecasting method provided by the invention not only increase the convergence rate of a training network, but also realizes a perfect forecasting effect; by combining the forecasting method with a migration method, the system overhead and the violation frequency of the service level agreement are effectively reduced, and the performance of network service is improved.

Description

technical field [0001] The invention belongs to the technical field of mobile communication, and relates to a virtual network function dynamic migration method based on deep belief network resource demand prediction. Background technique [0002] At present, the mobile network industry is rapidly evolving to 5G, and the three new application fields of "mobile broadband enhancement", "large-scale Internet of Things", and "low latency and high reliability communication" will play an important role. The 5G network has high flexibility to cope with the business changes of mobile operators, especially the concept of network function virtualization enables the infrastructure to flexibly meet the diversification of vertical application requirements. Network slicing is a technology for flexible configuration of resources in wireless virtual networks, which can be quickly deployed and managed centrally. It mainly uses software defined network (Software Defined Network, SDN) and netw...

AI Technical Summary

Problems solved by technology

Existing inventions have proved that neural network technology can well predict the relationship between resource characteristics and resource requirements. Although it shows that the prediction accuracy of neural network is higher than that of traditional statistical models, it does not involve the existence of neural network in the prediction process. Problems such as long training period, slow convergence speed and easy to fall into local minima

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