%0 Journal Article
%A 李公
%A 王培森
%A 张文超
%A 朱睿杰
%T 	Reinforced virtual optical network embedding algorithm in EONs for edge computing

%D 2022
%R 10.19682/j.cnki.1005-8885.2022.1020
%J 中国邮电高校学报（英文）
%P 18-29
%V 29
%N 6
%X <p>
	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.
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%U https://jcupt.bupt.edu.cn/CN/10.19682/j.cnki.1005-8885.2022.1020