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Hybrid Backup Resource Optimization for VNF Placement Over Optical Transport Networks Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Machine Learning Assisted Quality of Transmission Estimation and Planning with Reduced Margins

Authors : Konstantinos Christodoulopoulos, Ippokratis Sartzetakis, Polizois Soumplis, Emmanouel (Manos) Varvarigos

Published in: Optical Network Design and Modeling

Publisher: Springer International Publishing

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Abstract

In optical transport networks, the Quality of Transmission (QoT) using a physical layer model (PLM) is estimated before establishing new or reconfiguring established optical connections. Traditionally, high margins are added to account for the model’s inaccuracy and the uncertainty in the current and evolving physical layer conditions, targeting uninterrupted operation for several years, until the end-of-life (EOL). Reducing the margins increases network efficiency but requires accurate QoT estimation. We present two machine learning (ML) assisted QoT estimators that leverage monitoring data of existing connections to understand the actual physical layer conditions and achieve high estimation accuracy. We then quantify the benefits of planning/upgrading a network over multiple periods with accurate QoT estimation as opposed to planning with EOL margins.

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previous chapter Machine Learning Assisted Optical Network Resource Scheduling in Data Center Networks
next chapter Network Programmability and Automation in Optical Networks
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Metadata
Title
Machine Learning Assisted Quality of Transmission Estimation and Planning with Reduced Margins
Authors
Konstantinos Christodoulopoulos
Ippokratis Sartzetakis
Polizois Soumplis
Emmanouel (Manos) Varvarigos
Copyright Year
2020
Book
Optical Network Design and Modeling

Print ISBN: 978-3-030-38084-7

Electronic ISBN: 978-3-030-38085-4

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-38085-4_19

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