Dynamic Service Function Chain Migration Method Based on Resource Requirements Prediction

Aiming at the problem of network overload caused by the change of resource requirements of service function chain (SFC) under network function virtualization (NFV) environment, a dynamic SFC migration method based on resource requirements prediction (RRP-DSFCM) is proposed. Firstly, the migration overhead and resource overhead are considered comprehensively, and the physical network overhead model is established. Secondly, the resource requirements sequence is decomposed into intrinsic mode function (IMF) component and residual component by empirical mode decomposition (EMD), and each component is predicted by radial basis function (RBF) neural network. Particle swarm optimization (PSO) algorithm is used in the training process of neural network to optimize the parameters. Finally, for the physical nodes or links that will be overloaded in next timeslot, the virtual network function (VNF) or virtual link (that takes up the most overload resources) is selected to move out, and based on the principle of traffic optimization, the physical nodes which can minimize the overhead of the physical network are selected to move in through the awareness of the global network topology. The simulation results show that the proposed resource requirements prediction model can shorten the prediction time while improving the prediction accuracy, and the proposed SFC migration method has good performance in reducing the physical network overhead and the end-to-end delay and improving the reliability of the SFC.