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.

An Edge-Based Approach for Virtual Network Embedding Based on the Graph Edit Distance

Virtualization and resource isolation techniques have enabled the efficient sharing of networked resources. How to control network resource allocation accurately and flexibly has gradually become a research hotspot due to the growth in user demands. Therefore, this paper presents a new edge-based virtual network embedding approach to studying this problem that employs a graph edit distance method to accurately control resource usage. In particular, to manage network resources efficiently, we restrict the use conditions of network resources and restrict the structure based on common substructure isomorphism and an improved spider monkey optimization algorithm is employed to prune redundant information from the substrate network. Experimental results showed that the proposed method achieves better performance than existing algorithms in terms of resource management capacity, including energy savings and the revenue-cost ratio.