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SCIENCE CHINA Information Sciences, Volume 63 , Issue 6 : 160304(2020) https://doi.org/10.1007/s11432-020-2838-6

Intent defined optical network with artificial intelligence-based automated operation and maintenance

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  • ReceivedJan 10, 2020
  • AcceptedMar 16, 2020
  • PublishedMay 9, 2020

Abstract

Traditionally, the operation and maintenance of optical networks rely on the experience of engineers to configure network parameters, involving command-line interface, middle-ware scripting, and troubleshooting. However, with the emerging of newly B5G applications, the traditional configuration cannot meet the requirement of real-time automatic configuration. Operators need a new configuration way without manual intervention at an underlying optical transport network. To cope with this issue, we propose an intent defined optical network (IDON) architecture toward artificial intelligence-based optical network automated operation and maintenance against service objective, by introducing a self-adapted generation and optimization (SAGO) policy in a customized manner. The IDON platform has three key innovations including intent-orient configuration translation, self-adapted generation and optimization policy, and close-loop intent guarantee operation. Focusing specifically on communication requirements, the IDON uses natural language processing to construct semantic graphs to understand, interact, and create the required network configuration. Then, deep reinforcement learning (DRL) is utilized to find the composition policy that satisfies the requirement of intent through the dynamic integration of fine-grained policies. Finally, the deep neural evolutionary network (DNEN) is introduced to achieve the intent guarantee at the milliseconds level. The feasibility and efficiency are verified on enhanced SDN testbed. Finally, we discuss several related challenges and opportunities for unveiling a promising upcoming future of intent defined optical network.


Acknowledgment

This work was supported in part by National Natural Science Foundation of China (Grant No. 61871056), Young Elite Scientists Sponsorship Program by CAST (Grant No. 2018QNRC001), Beijing Natural Science Foundation (Grant No. 4202050), Fundamental Research Funds for the Central Universities (Grant Nos. 2018XKJC06, 2019PTB-009), Fund of SKL of IPOC (BUPT) (Grant Nos. IPOC2018A001, IPOC2019ZT01), ZTE Research Fund, and Key Laboratory Fund (Grant Nos. 6142411182112, 614210419042, 61400040503, CEPNT-2017KF-04).


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