4.7 Article

RKD-VNE: Virtual network embedding algorithm assisted by resource knowledge description and deep reinforcement learning in IIoT scenario

Publisher

ELSEVIER
DOI: 10.1016/j.future.2022.05.008

Keywords

Industrial Internet of Things; Virtual network embedding; Social attribute perception; Virtual network security; Resource knowledge description; Deep reinforcement learning

Funding

  1. Shandong Provincial Natural Science Foundation, China [ZR2020 MF006]
  2. Industry-university Research Innovation Foundation of Ministry of Education of China [2021FNA01001]
  3. Major Scientific and Technological Projects of CNPC, China [ZD2019-183006]
  4. Open Foundation of State Key Laboratory of Integrated Services Networks (Xidian University), China [ISN23-09]
  5. Zhejiang Provincial Natural Science Foundation of China [LZ22F020002]

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This paper proposes a virtual network embedding (VNE) algorithm assisted by resource knowledge description (RKD) and deep reinforcement learning (DRL) to address the requirements of efficient network resource utilization and security in industrial Internet of Things (IIoT) applications. By using social attribute perception to measure the security of physical nodes, standardizing resource constraints with RKD, and using a DRL agent to derive the probability of physical node embedding, the algorithm shows significant advantages in VNE, as demonstrated by simulation experiments.
In the era of Industry 4.0, the Industrial Internet of Things (IIoT) is developing rapidly, various IIoT applications pose new challenges to the existing network architecture. On the one hand, these applications put forward higher requirements for the efficient use of network resources. On the other hand, these applications generate massive amounts of information, and they pursue a more secure network environment. Therefore, in order to ensure security while effectively allocating network resources, this paper puts forward a virtual network embedding (VNE) algorithm assisted by resource knowledge description (RKD) and deep reinforcement learning (DRL). First, we use social attribute perception to measure the security of each physical node and regard it as one of the attributes of the physical node. Then, RKD is used to standardize resource constraints before the virtual network is embedded. Finally, the DRL agent derives the probability of the physical node being embedded according to the physical network attributes, and embeds the virtual node according to the probability. Simulation experiments show that compared with the BaseLine algorithm, the RKD-VNE algorithm proposed in this paper has obvious advantages in the general performance of VNE, especially in terms of long-term revenue consumption rate increased by 24.3%. (C) 2022 Elsevier B.V. All rights reserved.

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