Dealing With Alarms in Optical Networks Using an Intelligent System

Millions of alarms in the optical layer may appear in optical transport networks every month, which brings great challenges to network operation, administration and maintenance. In this paper, we deal with this problem and propose a method of alarm pre-processing and correlation analysis for this network. During the alarm pre-processing, we use the method of combined time series segmentation and time sliding window to extract the alarm transactions, and then we use the algorithm of combined $K$ -means and back propagation neural network to evaluate the alarm importance quantitatively. During the alarm correlation analysis, we modify a classic rule mining algorithm, i.e., Apriori algorithm, into a Weighted Apriori to find the high-frequency chain alarm sets among the alarm transactions. Through the actual alarm data from the record in the optical layer of a provincial backbone of China Telecom, we conducted experiments and the results show that our method is able to perform effectively the alarm compressing, alarm correlating, and chain alarm mining. By parameter adjustment, the alarm compression rate is able to vary from 60% to 90% and the average fidelity of chain alarm mining keeps around 84%. The results show our approach and method is promising for trivial alarm identifying, chain alarm mining, and root fault locating in existing optical networks.

[1]  Jian Wu,et al.  An Effective Mining Algorithm for Weighted Association Rules in Communication Networks , 2008, J. Comput..

[2]  Zabih Ghassemlooy,et al.  SVM detection for superposed pulse amplitude modulation in visible light communications , 2016, 2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP).

[3]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.

[4]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[5]  G. Grahne,et al.  High Performance Mining of Maximal Frequent Itemsets Gösta , 2003 .

[6]  Min Zhang,et al.  Nonlinearity Mitigation Using a Machine Learning Detector Based on $k$ -Nearest Neighbors , 2016, IEEE Photonics Technology Letters.

[7]  Min Zhang,et al.  Machine Learning Enabling Traffic-Aware Dynamic Slicing for 5G Optical Transport Networks , 2018, 2018 Conference on Lasers and Electro-Optics (CLEO).

[8]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[9]  Min Zhang,et al.  Adaptive Demodulator Using Machine Learning for Orbital Angular Momentum Shift Keying , 2017, IEEE Photonics Technology Letters.

[10]  Min Zhang,et al.  Intelligent constellation diagram analyzer using convolutional neural network-based deep learning. , 2017, Optics express.

[11]  Min Zhang,et al.  Nonlinear decision boundary created by a machine learning-based classifier to mitigate nonlinear phase noise , 2015, 2015 European Conference on Optical Communication (ECOC).

[12]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[13]  Ada Wai-Chee Fu,et al.  Mining association rules with weighted items , 1998, Proceedings. IDEAS'98. International Database Engineering and Applications Symposium (Cat. No.98EX156).

[14]  M. H. Margahny,et al.  FAST ALGORITHM FOR MINING ASSOCIATION RULES , 2014 .

[15]  Tong Wang,et al.  Database Encoding and An Anti-Apriori Algorithm for Association Rules Mining , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[16]  Min Zhang,et al.  Failure prediction using machine learning and time series in optical network. , 2017, Optics express.

[17]  Liang Yan,et al.  Incorporating Pageview Weight into an Association-Rule-Based Web Recommendation System , 2006, Australian Conference on Artificial Intelligence.

[18]  Eamonn J. Keogh,et al.  Segmenting Time Series: A Survey and Novel Approach , 2002 .

[19]  J H Lubin,et al.  The use of sliding time windows for the exploratory analysis of temporal effects of smoking histories on lung cancer risk. , 2000, Statistics in medicine.

[20]  Heikki Mannila,et al.  Rule Discovery in Telecommunication Alarm Data , 1999, Journal of Network and Systems Management.

[21]  N. Amani,et al.  A case-based reasoning method for alarm filtering and correlation in telecommunication networks , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

[22]  Xue Chen,et al.  Intelligent Optical Spectrum Analyzer Using Support Vector Machine , 2018, 2018 IEEE Photonics Society Summer Topical Meeting Series (SUM).

[23]  Min Zhang,et al.  System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm , 2017 .