Predicting network outages based on Q-drop in optical network

Yohei Hasegawa, Masato Uchida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


The sudden drop in the quality of an optical signal, called Q-drop, is an important factor for predicting network outages. Herein, we classify sudden drop events into two classes: one the results in a network outage, and one that does not result in a network outage, for the same period of time. Therefore, we build a predictor based on machine learning. The features of the predictor are given by characterizing the Q-drop event based on the optical layer characteristics immediately before the Q-drop event. The predictor is trained for each Q-drop event to adapt to the temporal change of the optical layer characteristics. Additionally, oversampling is applied to training data to avoid overlooking the network outage in the prediction. From the evaluation using real data, we showed that the proposed method is effective for the prediction of network outages in a short period. Furthermore, we found that information regarding the instability of the optical signal is important for the prediction of network outages. The result herein can contribute to improving the availability of the network because the proposed method predicts network outages based on characteristics that are invisible from the IP layer.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
EditorsVladimir Getov, Jean-Luc Gaudiot, Nariyoshi Yamai, Stelvio Cimato, Morris Chang, Yuuichi Teranishi, Ji-Jiang Yang, Hong Va Leong, Hossian Shahriar, Michiharu Takemoto, Dave Towey, Hiroki Takakura, Atilla Elci, Susumu Takeuchi, Satish Puri
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781728126074
Publication statusPublished - 2019 Jul
Event43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 - Milwaukee, United States
Duration: 2019 Jul 152019 Jul 19

Publication series

NameProceedings - International Computer Software and Applications Conference
ISSN (Print)0730-3157


Conference43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019
Country/TerritoryUnited States


  • Machine learning
  • Optical network
  • Outage prediciton
  • Q-drop

ASJC Scopus subject areas

  • Software
  • Computer Science Applications


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