Reinforced virtual optical network embedding algorithm in EONs for edge computing

被引：0

作者：

Zhu Ruijie ^{[1
]}

Li Gong ^{[2
]}

Wang Peisen ^{[1
]}

Zhang Wenchao ^{[1
]}

机构：

[1] School of Computer and Artificial Intelligence,Zhengzhou University

[2] Henan Institute of Advanced Technology,Zhengzhou University

来源：

TheJournalofChinaUniversitiesofPostsandTelecommunications | 2022年 / 29卷 / 06期

关键词：

D O I：

10.19682/j.cnki.1005-8885.2022.1020

中图分类号：

TN929.1 [光波通信、激光通信];

学科分类号：

0803 ;

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.

引用

页码：18 / 29

页数：12

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