  The development of Intelligent Cyber-Physical Systems (ICPSs) in virtual
network environment is facing severe challenges. On the one hand, the Internet
of things (IoT) based on ICPSs construction needs a large amount of reasonable
network resources support. On the other hand, ICPSs are facing severe network
security problems. The integration of ICPSs and network virtualization (NV) can
provide more efficient network resource support and security guarantees for IoT
users. Based on the above two problems faced by ICPSs, we propose a virtual
network embedded (VNE) algorithm with computing, storage resources and security
constraints to ensure the rationality and security of resource allocation in
ICPSs. In particular, we use reinforcement learning (RL) method as a means to
improve algorithm performance. We extract the important attribute
characteristics of underlying network as the training environment of RL agent.
Agent can derive the optimal node embedding strategy through training, so as to
meet the requirements of ICPSs for resource management and security. The
embedding of virtual links is based on the breadth first search (BFS) strategy.
Therefore, this is a comprehensive two-stage RL-VNE algorithm considering the
constraints of computing, storage and security three-dimensional resources.
Finally, we design a large number of simulation experiments from the
perspective of typical indicators of VNE algorithms. The experimental results
effectively illustrate the effectiveness of the algorithm in the application of
ICPSs.
