Abstract

In this paper, we propose a performance monitoring and failure prediction method in optical networks based on machine learning. The primary algorithms of this method are the support vector machine (SVM) and double exponential smoothing (DES). With a focus on risk-aware models in optical networks, the proposed protection plan primarily investigates how to predict the risk of an equipment failure. To the best of our knowledge, this important problem has not yet been fully considered. Experimental results showed that the average prediction accuracy of our method was 95% when predicting the optical equipment failure state. This finding means that our method can forecast an equipment failure risk with high accuracy. Therefore, our proposed DES-SVM method can effectively improve traditional risk-aware models to protect services from possible failures and enhance the optical network stability.

© 2017 Optical Society of America

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Corrections

25 July 2017: Typographical corrections were made to paragraph 4 of Section 1, paragraph 2–4 and 10 of Section 2.A, paragraph 4 and 5 of Section 2.B, paragraph 1 of Section 3, Algorithm 1, Refs. 8–18, and the funding section..


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References

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    [Crossref]
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    [Crossref] [PubMed]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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  13. S. Suthaharan, “Support Vector Machine,” in Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, S. Suthaharan, (Springer US, 2016).
  14. D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
    [Crossref]
  15. T. Knebel, S. Hochreiter, and K. Obermayer, “An SMO algorithm for the potential support vector machine,” Neural Comput. 20(1), 271–287 (2008).
    [Crossref] [PubMed]
  16. A. C. Adamuthe, R. A. Gage, G. T. Thampi, and Ieee, “Forecasting Cloud Computing using Double Exponential Smoothing Methods,” in Proc. Advanced Computing and Communication Systems (ACCS 2015), pp. 5.
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    [Crossref] [PubMed]

2017 (2)

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).
[Crossref]

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
[Crossref]

2016 (3)

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

X. Li, S. Huang, S. Yin, B. Guo, Y. Zhao, J. Zhang, M. Zhang, and W. Gu, “Shared end-to-content backup path protection in k-node (edge) content connected elastic optical datacenter networks,” Opt. Express 24(9), 9446–9464 (2016).
[Crossref] [PubMed]

2014 (2)

2011 (1)

2008 (1)

T. Knebel, S. Hochreiter, and K. Obermayer, “An SMO algorithm for the potential support vector machine,” Neural Comput. 20(1), 271–287 (2008).
[Crossref] [PubMed]

2003 (2)

S. S. Keerthi and C. J. Lin, “Asymptotic behaviors of support vector machines with Gaussian kernel,” Neural Comput. 15(7), 1667–1689 (2003).
[Crossref] [PubMed]

S. Ramamurthy, L. Sahasrabuddhe, and B. Mukherjee, “Survivable WDM mesh networks,” J. Lightwave Technol. 21(4), 870–883 (2003).
[Crossref]

Bai, Y.

Cai, Z.

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

Chen, X.

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
[Crossref]

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).
[Crossref]

Cheng, X.

Cui, Y.

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

Dikbiyik, F.

Fu, M.

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).
[Crossref]

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

Gu, W.

Guo, B.

Han, H.

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

Hochreiter, S.

T. Knebel, S. Hochreiter, and K. Obermayer, “An SMO algorithm for the potential support vector machine,” Neural Comput. 20(1), 271–287 (2008).
[Crossref] [PubMed]

Huang, S.

Keerthi, S. S.

S. S. Keerthi and C. J. Lin, “Asymptotic behaviors of support vector machines with Gaussian kernel,” Neural Comput. 15(7), 1667–1689 (2003).
[Crossref] [PubMed]

Knebel, T.

T. Knebel, S. Hochreiter, and K. Obermayer, “An SMO algorithm for the potential support vector machine,” Neural Comput. 20(1), 271–287 (2008).
[Crossref] [PubMed]

Li, J.

Li, X.

Li, Z.

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).
[Crossref]

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
[Crossref]

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

Lin, C. J.

S. S. Keerthi and C. J. Lin, “Asymptotic behaviors of support vector machines with Gaussian kernel,” Neural Comput. 15(7), 1667–1689 (2003).
[Crossref] [PubMed]

Luo, B.

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

Mukherjee, B.

Ngoh, L. H.

Obermayer, K.

T. Knebel, S. Hochreiter, and K. Obermayer, “An SMO algorithm for the potential support vector machine,” Neural Comput. 20(1), 271–287 (2008).
[Crossref] [PubMed]

Ramamurthy, S.

Sahasrabuddhe, L.

Shao, X.

Song, C.

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).
[Crossref]

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
[Crossref]

Tornatore, M.

Wang, D.

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).
[Crossref]

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
[Crossref]

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

Yeo, Y.-K.

Yin, S.

Zhang, J.

Zhang, M.

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).
[Crossref]

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
[Crossref]

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

X. Li, S. Huang, S. Yin, B. Guo, Y. Zhao, J. Zhang, M. Zhang, and W. Gu, “Shared end-to-content backup path protection in k-node (edge) content connected elastic optical datacenter networks,” Opt. Express 24(9), 9446–9464 (2016).
[Crossref] [PubMed]

Zhao, Y.

Zhou, L.

IEEE Photonics Technol. Lett. (1)

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

J. Lightwave Technol. (2)

J. Opt. Commun. Netw. (1)

Neural Comput. (2)

T. Knebel, S. Hochreiter, and K. Obermayer, “An SMO algorithm for the potential support vector machine,” Neural Comput. 20(1), 271–287 (2008).
[Crossref] [PubMed]

S. S. Keerthi and C. J. Lin, “Asymptotic behaviors of support vector machines with Gaussian kernel,” Neural Comput. 15(7), 1667–1689 (2003).
[Crossref] [PubMed]

Opt. Commun. (2)

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).
[Crossref]

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

Opt. Express (3)

Other (7)

Y. Yuan, M. Zhang, P. Luo, Z. Ghassemlooy, D. Wang, X. Tang, and D. Han, “SVM detection for superposed pulse amplitude modulation in visible light communications,” in Proceedings of Communication Systems, Networks and Digital Signal Processing (CSNDSP 2016), pp. 1–5.

E. Osuna, R. Freund, and F. Girosi, “Support vector machines: Training and applications,” AI Memo, AIM-1602 (1997).

S. Suthaharan, “Support Vector Machine,” in Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, S. Suthaharan, (Springer US, 2016).

H. Weigang, G. Lei, Y. Cunqian, and Z. Yue, “Risk-aware virtual network embedding in optical data center networks,” in Proceedings of OptoElectronics and Communications Conference (OECC 2016), pp. 1–3.

D. Wang, M. Zhang, Z. Li, Y. Cui, J. Liu, Y. Yang, and H. Wang, “Nonlinear decision boundary created by a machine learning-based classifier to mitigate nonlinear phase noise,” in Proceedings of Optical Communication (ECOC 2015), pp. 1–3.

A. C. Adamuthe, R. A. Gage, G. T. Thampi, and Ieee, “Forecasting Cloud Computing using Double Exponential Smoothing Methods,” in Proc. Advanced Computing and Communication Systems (ACCS 2015), pp. 5.

C. Hsu, C. C. Chang, and C. J. Lin, “A practical guide to support vector classification.” http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

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Figures (10)

Fig. 1
Fig. 1 The main principles of SVM.
Fig. 2
Fig. 2 Combination of the DES and SVM methods.
Fig. 3
Fig. 3 DES-SVM method predicting an equipment failure and the risk-aware protection algorithm preventing data loss in SDMAN.
Fig. 4
Fig. 4 Classification accuracy according to a single indicator reflex the related degree between this indicator and equipment failure
Fig. 5
Fig. 5 Relation between indicators and board failure.
Fig. 6
Fig. 6 The affection of number of indicators to model accuracy.
Fig. 7
Fig. 7 SVM model accuracy with different kernel function and punishment factor C.
Fig. 8
Fig. 8 Discrepancy between the actual and predicted values when using DES.
Fig. 9
Fig. 9 Number of predicted fault boards compared with actual fault boards and prediction accuracy of DES-SVM.
Fig. 10
Fig. 10 Prediction result based on different number of training days.

Tables (2)

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Algorithm 1 N-fold cross-validation algorithm

Tables Icon

Table 1 Indicators In Board Data

Equations (18)

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y ( x ) = w T x + b
arg max w , b { 2 w min n [ l n ( w T x n + b ) ] }
arg min w , b { 1 2 w 2 }
s . t . l n y ( x ) 1 , n = 1 , 2 , , N
arg max w , b { i = 1 N a i 1 2 i , j = 1 N l i l j a i a j x i , x j } ,
s . t . a i 0 , i = 1 , 2 , , N ,
i = 1 N a i l i = 0 .
w = i = 1 N a i l i x i
b = l j i = 1 N a i l i x i , x j
arg max w , b { i = 1 N a i 1 2 i , j = 1 N l i l j a i a j K ( x i , x j ) } ,
s . t . C a i 0 , i = 1 , 2 , , N ,
i = 1 N a i l i = 0 .
b = l j i = 1 N a i l i K ( x i , x j ) .
S t ( 1 ) = a y t + ( 1 a ) S t 1 ( 1 )
S t ( 2 ) = a S t ( 1 ) + ( 1 a ) S t 1 ( 2 )
Y ^ t + T = a t + b t T
a t = 2 S t ( 1 ) S t ( 2 )
b t = a 1 a ( S t ( 1 ) S t ( 2 ) )

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