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Multivariate Machine Learning Models for Short-Term Forecast of Lightpath Performance

Machine learning (ML) is emerging as a promising solution for managing the physical layer complexity of heterogeneous dynamic optical networks transporting multiple applications in a software defined network (SDN) context, namely for performance prediction. We propose two multivariate neural network models based on gated recurrent unit (GRU) and long short-term memory (LSTM) methods, trained with field performance data and features, for predicting lightpath signal-to-noise ratio (SNR) over forecast horizons of up to 4 days. The best performance is achieved by using a 5-feature LSTM multivariate model over forecast horizons of up to 96 hours, with an absolute maximum error (AME) of 0.90 dB, compared to 0.91 dB and 0.97 dB for the GRU and LSTM univariate models, respectively, and 1.21 dB for a persistence model. The 2-feature multivariate models obtained through feature engineering perform better than their univariate counterparts for forecast horizons of up to 40 hours. Lastly, we explore the concept of transfer learning (TL) by testing the trained multivariate LSTM and univariate GRU models on field data from two lightpaths carried on the same route. The TL models underperform the naive model for the lightpath carried in a different optical fiber. However, for the lightpath carried in the same optical fiber on a portion of the same route, the LSTM-based TL model outperforms the naive model with a difference of up to 0.11 dB at a 96-hour forecast horizon, compared to 0.30 dB for the lightpath in the source domain, while using 3 times less training data.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.39 No.22 November 15 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 7146-7158

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

TRANSFER LEARNING
MACHINE LEARNING (ML)
PERFORMANCE PREDICTION
QUALITY OF TRANSMISSION
LONG SHORT-TERM MEMORY (LSTM)
GATED RECURRENT UNIT (GRU)
MULTIVARIATE NEURAL NETWORK

Info Detail Spesifik

10.1109/JLT.2021.3110513

Pernyataan Tanggungjawab

Stephanie Allogba, Sandra Aladin, Christine Tremblay

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