Reinforced virtual optical network embedding algorithm in EONs for edge computing  

作　　者：Zhu Ruijie Li Gong Wang Peisen Zhang Wenchao 

机构地区：[1]School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China [2]Henan Institute of Advanced Technology,Zhengzhou University,Zhengzhou 450002,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2022年第6期18-29,共12页中国邮电高校学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(62001422);Henan Scientific and Technology Innovation Talents(22HASTIT016).

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 

分 类 号：TN929.1[电子电信—通信与信息系统]