An Identification Decision Tree Learning Model for Self-Management in Virtual Radio Access Network: IDTLM

Along with the blowout of new applications and the integration of the heterogeneous networks platforms in the future Internet of everything, the self-management of virtual radio access network is of significant importance. The urgent problem needed to be solved for the self-management in virtual radio access network is the match of application and virtual service (tailored service of virtual functions for the application). In this paper, an identification decision tree learning model (IDTLM) based on transfer learning has been proposed. First, we do research on the redundant problem of the traditional packet decision trees and reduce the dimensionality of features by a proximal gradient descent method and clustering the features by Lagrange's multiplier, so as to improve the online matching speed between applications and virtual service. And then, in consideration of the independent and non-identical distribution among online and trained data, and the possible change of virtualized network platform, a method of transfer learning is proposed to improve the quality of generalization for IDTLM. Finally, online test is done for IDTLM, and the result shows that the accuracy rate of trained applications can reach 99% and the accuracy rate can reach 96% if untrained applications are included. Meanwhile, theoretical analysis has been carried out for the transfer of IDTLM. The analysis shows that IDTLM system is of high recognizing speed and low false positive rate and it could adapt to the transfer of different scenarios.