基于遗传算法的服务功能链贪心备份方法研究OA

In network function virtualization(NFV)environments,failures of virtual network functions(VNFs)and their infrastructure can severely compromise the availability of service function chains(SFCs)and degrade overall network performance,posing a critical challenge in NFV architectures.To address this issue,this paper proposes a genetic algorithm-based greedy backup method for SFCs.First,a Generative Adversarial Network(GAN)-enabled edge node fault prediction model is developed to assess node health status,thereby avoiding the use of high-risk nodes for backup.Second,a greedy strategy prioritizes VNF instances that maximize system availability improvements while mini-mizing resource consumption,ensuring efficient backup selection.Third,leveraging iterative optimization via genetic algorithms,the method optimally deploys backup VNFs across nodes and links to guarantee SFC high availability.Simulation results demonstrate that,compared to the K-Shortest Path(KSP)method,the proposed approach reduces resource consumption by 32%,increases SFC request acceptance rates by 11%,and maintains an average backup ratio of 0.5.By integrating a closed-loop"prediction-sifting-optimization"mechanism,this method significantly enhances SFC robustness and resource utilization efficiency in dynamic network environments.