Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Knowledge management in optical networks: architecture, methods, and use cases [Invited]

Not Accessible

Your library or personal account may give you access

Abstract

Autonomous network operation realized by means of control loops, where prediction from machine learning (ML) models is used as input to proactively reconfigure individual optical devices or the whole optical network, has been recently proposed to minimize human intervention. A general issue in this approach is the limited accuracy of ML models due to the lack of real data for training the models. Although the training dataset can be complemented with data from lab experiments and simulation, it is probable that once in operation, events not considered during the training phase appear and thus lead to model inaccuracies. A feasible solution is to implement self-learning approaches, where model inaccuracies are used to re-train the models in the field and to spread such data for training models being used for devices of the same type in other nodes in the network. In this paper, we develop the concept of collective self-learning aiming at improving the model’s error convergence time as well as at minimizing the amount of data being shared and stored. To this end, we propose a knowledge management (KM) process and an architecture to support it. Besides knowledge usage, the KM process entails knowledge discovery, knowledge sharing, and knowledge assimilation. Specifically, knowledge sharing and assimilation are based on distributing and combining ML models, so specific methods are proposed for combining models. Two use cases are used to evaluate the proposed KM architecture and methods. Exhaustive simulation results show that model-based KM provides the best error convergence time with reduced data being shared.

© 2019 Optical Society of America

Full Article  |  PDF Article
More Like This
Learning Life Cycle to Speed Up Autonomic Optical Transmission and Networking Adoption

Luis Velasco, Behnam Shariati, Fabien Boitier, Patricia Layec, and Marc Ruiz
J. Opt. Commun. Netw. 11(5) 226-237 (2019)

AI/ML-as-a-Service for optical network automation: use cases and challenges [Invited]

Carlos Natalino, Ashkan Panahi, Nasser Mohammadiha, and Paolo Monti
J. Opt. Commun. Netw. 16(2) A169-A179 (2024)

Machine Learning for Network Automation: Overview, Architecture, and Applications [Invited Tutorial]

Danish Rafique and Luis Velasco
J. Opt. Commun. Netw. 10(10) D126-D143 (2018)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (12)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (3)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (5)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved