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Designing efficient and flexible approaches for placement of Virtual Network Function (VNF) chains is the main success of Network Function Virtualization (NFV). However, most current work considers the constant bandwidth and flow processing requirements while deploying the VNFs in the network. The constant (immutable) flow processing and bandwidth requirements become critical limitations in an NFV-enabled network with highly dynamic traffic flow. Therefore, bandwidth requirements and available resources of the Point-of-Presence (PoP) in the network change constantly. We present an adaptive model for placing VNF chains to overcome this limitation. At the same time, the proposed model minimizes the number of changes (i.e., re-allocation of VNFs) in the network. The experimental evaluation shows that the adaptive model can deliver stable network services. Moreover, it reduces the significant number of changes in the network and ensures flow performance.

SummaryNetwork function virtualization (NFV) is a name of technology for replacing hardware‐based network functions with software programs. Virtual network function (VNF) is a software program that replaces the hardware‐based network functionality. The replacement of the hardware‐based network functions (middleboxes) with software programs promises the on‐demand provisioning of network functions and reduces capital and operational expenses of the network. Due to this replaced network can adapt to the different network functions. Network service providers deploy various network services with different objectives, such as reducing the network's active servers and traffic latency or network operational expenses. In this article, a VNF placement problem is studied to optimize the total operating costs of the networks. To solve the VNF placement problem, we proposed an integer linear program (ILP) model, which has been implemented using CPLEX. Although an ILP‐based approach gives an optimal solution, it takes a long execution time to find the solution. Due to the long execution time, the ILP‐based approach is not suitable for the real‐time VNF placement problem. To address this challenge, we proposed a heuristic based on dynamic programming that performs better than the existing approaches. The simulation results of the proposed solution using real‐world topologies show that the heuristic approach finds a feasible solution that is only 1 to 1.34 times far from the optimal one. Moreover, experimental results show that the proposed heuristic is 15 to 423 times faster than the ILP.