CRLGNIFeb 3, 2022

Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm

arXiv:2202.01375v129 citations
AI Analysis

This addresses resource and security issues for IoT users in ICPSs, but it appears incremental as it builds on existing VNE methods with added constraints and RL.

The paper tackles resource management and security challenges in Intelligent Cyber-Physical Systems (ICPSs) and IoT by proposing a two-stage reinforcement learning-based virtual network embedding algorithm, with simulation results showing its effectiveness in improving resource allocation and security.

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.

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