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

TL;DR

This paper proposes a reinforcement learning-based virtual network embedding algorithm that optimizes resource allocation and enhances security for ICPSs and IoT environments, validated through extensive simulations.

Contribution

It introduces a novel RL-based VNE algorithm considering computing, storage, and security constraints specifically for ICPSs and IoT applications.

Findings

The algorithm effectively improves resource management in ICPSs.

Simulation results demonstrate enhanced security and efficiency.

The method outperforms existing VNE algorithms in key metrics.

Abstract

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.