Survivable SFC deployment method based on federated learning in multi-domain network

Survivable SFC deployment method based on federated learning in multi-domain network

연구 분야: Software Development

학회: The Journal of Supercomputing

In the multi-domain network scenario, in order to improve the survivability of service function chain (SFC) in the face of network failure, most methods solve this problem through virtual network function (VNF) backup mechanism. However, the traditional multi-domain SFC deployment method lacks a SFC partition mechanism for backup resource consumption and does not consider the isolation and privacy requirements between different network domains. In view of the above problems, this paper proposes a reliability partition scheme based on reinforcement learning in SFC partition stage, which can ensure that VNF is backed up while maintaining good load balancing and low inter-domain transmission delay, and improve the reliability of SFC. Then, this paper proposes a VNF backup mechanism with minimum resource fluctuation in the VNF mapping stage and uses the integer linear programming (ILP) model to determine the backup scheme of each VNF, so as to ensure the minimum fluctuation of resource occupancy of the entire network. Finally, this paper proposes a multi-domain SFC deployment and backup algorithm based on Federated learning (FA-MSDB). The experimental results indicate that FA-MSDB can effectively improve the survival rate of SFC, reduce the overall transmission delay, and ensure good inter-domain and intra-domain load balance.