Abstract
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 language | English |
|---|---|
| Title of host publication | Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019 |
| Editors | Vladimir 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 |
| Publisher | IEEE Computer Society |
| Pages | 258-263 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728126074 |
| DOIs | |
| Publication status | Published - 2019 Jul |
| Event | 43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 - Milwaukee, United States Duration: 2019 Jul 15 → 2019 Jul 19 |
Publication series
| Name | Proceedings - International Computer Software and Applications Conference |
|---|---|
| Volume | 1 |
| ISSN (Print) | 0730-3157 |
Conference
| Conference | 43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 |
|---|---|
| Country | United States |
| City | Milwaukee |
| Period | 19/7/15 → 19/7/19 |
Fingerprint
Keywords
- Machine learning
- Optical network
- Outage prediciton
- Q-drop
ASJC Scopus subject areas
- Software
- Computer Science Applications
Cite this
Predicting network outages based on Q-drop in optical network. / Hasegawa, Yohei; Uchida, Masato.
Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019. ed. / Vladimir 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. IEEE Computer Society, 2019. p. 258-263 8754055 (Proceedings - International Computer Software and Applications Conference; Vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Predicting network outages based on Q-drop in optical network
AU - Hasegawa, Yohei
AU - Uchida, Masato
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Machine learning
KW - Optical network
KW - Outage prediciton
KW - Q-drop
UR - http://www.scopus.com/inward/record.url?scp=85072693089&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072693089&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC.2019.00045
DO - 10.1109/COMPSAC.2019.00045
M3 - Conference contribution
AN - SCOPUS:85072693089
T3 - Proceedings - International Computer Software and Applications Conference
SP - 258
EP - 263
BT - Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
A2 - Getov, Vladimir
A2 - Gaudiot, Jean-Luc
A2 - Yamai, Nariyoshi
A2 - Cimato, Stelvio
A2 - Chang, Morris
A2 - Teranishi, Yuuichi
A2 - Yang, Ji-Jiang
A2 - Leong, Hong Va
A2 - Shahriar, Hossian
A2 - Takemoto, Michiharu
A2 - Towey, Dave
A2 - Takakura, Hiroki
A2 - Elci, Atilla
A2 - Takeuchi, Susumu
A2 - Puri, Satish
PB - IEEE Computer Society
ER -