Reinforced virtual optical network embedding algorithm in EONs for edge computing

doi:10.19682/j.cnki.1005-8885.2022.1020

中国邮电高校学报(英文) ›› 2022, Vol. 29 ›› Issue (6): 18-29.doi: 10.19682/j.cnki.1005-8885.2022.1020

• Special Topic: Optical Communication and Artificial Intelligence • 上一篇下一篇

Reinforced virtual optical network embedding algorithm in EONs for edge computing

Zhu Ruijie, Li Gong, Wang Peisen, Zhang Wenchao

1. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China 2. Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450002, China

收稿日期:2022-07-21 修回日期:2022-10-18 出版日期:2022-12-30 发布日期:2022-12-30
通讯作者: Zhu Ruijie E-mail:zhuruijie@zzu.edu.cn
基金资助:
This work was supported in part by the National Natural Science Foundation of China (62001422), and Henan Scientific and Technology Innovation Talents (22HASTIT016).

Reinforced virtual optical network embedding algorithm in EONs for edge computing

Zhu Ruijie, Li Gong, Wang Peisen, Zhang Wenchao

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 E-mail:zhuruijie@zzu.edu.cn
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).

摘要/Abstract

摘要：

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.

关键词:

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

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

中图分类号:

TP393.0

Zhu Ruijie, Li Gong, Wang Peisen, Zhang Wenchao. Reinforced virtual optical network embedding algorithm in EONs for edge computing[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(6): 18-29.

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Reinforced virtual optical network embedding algorithm in EONs for edge computing

Reinforced virtual optical network embedding algorithm in EONs for edge computing

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