An Integrity Measurement Scheme for Containerized Virtual Network Function

The deployment of virtual network function (VNF) in the container can realize the 5G service-based architecture (SBA) with high flexibility. The container carrying the VNF has poor isolation and low protection capabilities, and there is a security risk of being tampered and replaced. Current security protection technologies such as access control, intrusion detection, and virus detection cannot ensure that the container is not illegally modified. In order to fundamentally protect the integrity of containerized VNFs, this paper proposes a containerized VNF trust measurement scheme container integrity measurement (CIM). The scheme extends the chain of trust to bare metal containers and virtual machine containers, and experiments are carried out in a containerized VNF communication environment. The results show that the integrity measurement protection scheme is effective. Compared with ordinary containers, the average CPU usage of trusted containers has increased by 26%, and the average memory usage growth rate is less than 1%, the performance overhead caused by CIM is acceptable.

Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment

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.