Software-defined networking (SDN) and network functions virtualization (NFV) enable service providers to accommodate diversified service requests in the fifth generation (5G) core networks. Given the time-varying traffic demand of the service requests, it is crucial for service providers to embed the service function chains (SFCs) of the service requests in the network to support load balancing, and to minimize the reconfiguration overhead due to virtual network functions (VNFs) migration while satisfying their quality of service (QoS) requirements. In this paper, we study a delay-aware VNF migration problem for embedding SFCs in a network with limited processing resource capacity for NFV-enabled nodes. We formulate it as a mixed-integer nonlinear optimization problem. We decompose this problem into two subproblems for stateful VNF mapping and allocation of processing resources, where the second subproblem is a convex optimization problem. To solve the first subproblem, we propose an algorithm based on deep neural network (DNN) with attention mechanism for learning the stochastic policy of a near-optimal VNF mapping. Simulation results show that our proposed algorithm provides a solution which is very close to the optimal solution obtained by solving a mixed-integer quadratically constrained programming problem.
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Service Function Chain Reconfiguration in 5G Core Networks Using Deep Learning
Semantic Scholar · Computer Science · 2021
Abstract
Software-defined networking (SDN) and network functions virtualization (NFV) enable service providers to accommodate diversified service requests in the fifth generation (5G) core networks. Given the time-varying traffic demand of the service requests, it is crucial for service providers to embed the service function chains (SFCs) of the service requests in the network to support load balancing, and to minimize the reconfiguration overhead due to virtual network functions (VNFs) migration while satisfying their quality of service (QoS) requirements. In this paper, we study a delay-aware VNF migration problem for embedding SFCs in a network with limited processing resource capacity for NFV-enabled nodes. We formulate it as a mixed-integer nonlinear optimization problem. We decompose this problem into two subproblems for stateful VNF mapping and allocation of processing resources, where the second subproblem is a convex optimization problem. To solve the first subproblem, we propose an algorithm based on deep neural network (DNN) with attention mechanism for learning the stochastic policy of a near-optimal VNF mapping. Simulation results show that our proposed algorithm provides a solution which is very close to the optimal solution obtained by solving a mixed-integer quadratically constrained programming problem.