Abstract: As a key technology to improve the utilization of network resources,service function chain combined with deep reinforcement learning makes it possible to achieve flexible and secure deployment.However,how to effectively deploy service function chains with security requirements while maximizing long-term average revenue is an important challenge it faces.This paper proposes a deployment method for security-aware service function chain based on deep reinforcement learning(DRL-SASFCD).Firstly,a security-aware mechanism is proposed to evaluate the credibility of physical network nodes,and a security requirement index is introduced to perceive the security requirements of SFC.Secondly,this method utilizes graph attention network and sequence to sequence models to extract relevant features of underlying physical network information and service function chain request sequence information.It generates service function chain deployment strategies based on these features.Finally,the proximal policy optimization method is adopted to optimize the policy and training network parameters.By limiting the update amplitude between the new and old policies,the drastic fluctuations during the policy update process are avoided,thereby improving the efficiency of security policy optimization.The simulation results show that DRL-SASFCD can improve the deployment acceptance rate,long-term average revenue and long-term average revenue-cost ratio compared with the existing methods while considering the security requirements of service function chain deployment.

Key words: Service function chain,Virtual network function,Deep reinforcement learning,Security,Deployment revenue