Abstract
Network function virtualization (NFV) has the potential to lead to significant reductions in capital expenditure and can improve the flexibility of the network. Virtual network function (VNF) deployment problem will be one of key problems that need to be addressed in NFV. To solve the problem of routing and VNF deployment, an optimization model, which minimizes the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF, is established. In this optimization model, the dependency among the different VNFs is considered. In order to solve the service chain mapping problem of high dynamic virtual network, a new virtual network function service chain mapping algorithm PDQN-VNFSC was proposed by combining prediction algorithm and DQN (Deep Q-Network). Firstly, the real-time mapping of virtual network service chains is modeled into a partial observable Markov decision process. Then, the real-time mapping process of virtual network service chain is optimized by using global and long-term benefits. Finally, the service chain of virtual network function is mapped through the learning decision framework of offline learning and online deployment. The simulation results show that, compared with the existing algorithms, the proposed algorithm has a lower the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF.
Highlights
In the traditional network, in order to provide a variety of network services, operators need to deploy a large number of monitors, load balancers, firewalls, intrusion detection systems, and other different network functions. ese network functions (NF) generally require specific devices to be physically deployed to realize, and the network data flow of some network functions that need to cross is called the network function chain [1,2,3]
Virtual network function configuration in the virtual network function chain is a key problem to be solved in the implementation of network function virtualization, and how to solve the problem of virtual network function configuration is a key to solve the problem of network function virtualization [9,10,11]
Baojia et al [21] study the problem of virtual network function deployment and propose a deep reinforcement learning method based on AC(Actor-Critic), which can well obtain the deployment scheme of virtual network function according to the current network state
Summary
In order to provide a variety of network services, operators need to deploy a large number of monitors, load balancers, firewalls, intrusion detection systems, and other different network functions. ese network functions (NF) generally require specific devices to be physically deployed to realize, and the network data flow of some network functions that need to cross is called the network function chain [1,2,3]. Under the background of network function virtualization, the research on the resource scheduling scheme oriented to virtual network function service chain is mainly based on three kinds of methods: heuristic method [10, 16], optimization model method [17], and learning theory-based method [18]. In order to get the approximate optimal mapping scheme of virtual network function service chain, Quang et al [18] proposed a solution method based on reinforcement learning search in a large action space to get the optimal mapping scheme. Baojia et al [21] study the problem of virtual network function deployment and propose a deep reinforcement learning method based on AC(Actor-Critic), which can well obtain the deployment scheme of virtual network function according to the current network state.
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