The Journal of China Universities of Posts and Telecommunications ›› 2022, Vol. 29 ›› Issue (6): 18-29.doi: 10.19682/j.cnki.1005-8885.2022.1020

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Reinforced virtual optical network embedding algorithm in EONs for edge computing

Zhu Ruijie, Li Gong, Wang Peisen, Zhang Wenchao   

  1. 1. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China    2. Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450002, China
  • Received:2022-07-21 Revised:2022-10-18 Online:2022-12-30 Published:2022-12-30
  • Contact: Zhu Ruijie
  • Supported by:
    This work was supported in part by the National Natural Science Foundation of China (62001422), and Henan Scientific and Technology Innovation Talents (22HASTIT016).

Abstract: As the core technology of optical networks virtualization, virtual optical network embedding ( VONE) enables multiple virtual network requests to share substrate elastic optical network ( EON) resources simultaneously and hence has been applicated in edge computing scenarios. In this paper, we propose a reinforced virtual optical network embedding ( R-VONE ) algorithm based on deep reinforcement learning ( DRL) to optimize network embedding policies automatically. The network resource attributes are extracted as the environment state for model training, based on which DRL agent can deduce the node embedding probability. Experimental results indicate that R-VONE presents a significant advantage with lower blocking probability and higher resource utilization.

Key words: elastic optical network (EON), deep reinforcement learning (DRL), virtual optical network embedding (VONE), edge computing

CLC Number: