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.
翻译:在虚拟网络环境中开发智能网络-物理系统(ICPS)正面临严峻的挑战,一方面,基于比较方案系统建设的互联网(IoT)需要大量合理的网络资源支持;另一方面,比较方案系统面临严重的网络安全问题;综合比较方案和网络虚拟化(NV)可以为国际比较方案用户提供更有效的网络资源支持和安全保障。根据比较方案面临的上述两个问题,我们提议建立一个虚拟网络内嵌的计算、储存资源和安全限制算法(VNE),以确保比较方案系统资源分配的合理性和安全性。特别是,我们利用强化学习(RL)方法作为提高算法绩效的手段。我们从基本网络的重要属性方面提取了作为比较方案代理机构的培训环境。代理可以通过培训获得最佳的节点嵌战略,从而满足比较方案系统资源管理和安全方面的需要。虚拟链接的嵌嵌入基于广度第一次搜索(BFS)战略,以确保比较方案系统资源分配的合理性和安全性。因此,我们利用强化学习(RL)方法的方法作为改进算法业绩的一种手段,从VL-NEA系统大规模安全性模型设计指标中,这是我们安全性地在VA级系统安全性试验应用中的一种综合的三阶段的模型。