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Analysis on route information failure in IP core networks by NFV-based test environment
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Authors: Xia Fei, Aerman Tuerxun, Jiaxing Lu, Ping Du, Akihiro Nakao
Status: Final
Date of publication: 27 August 2021
Published in: ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 4 - AI and machine learning solutions in 5G and future networks, Pages 101-112
Article DOI : https://doi.org/10.52953/NPYN3956
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 | Abstract:
Stable and high-quality Internet connectivity is mandatory for 5G mobile networks. However, the pandemic of COVID-19 has forced global and large-scale staying at home and telecommuting in many countries. The increasing traffic has induced more pressure on networks, devices and cloud data centers. It becomes an essential task for network operators to enable their ability to automatically and rapidly detect network and device failures. We propose a highly practical method based on highly practical technology. Our method has a high generalization ability that can efficiently extract features from large-scale unstructured data and ensure high accuracy prediction. First, 997 useful features are extracted from 28GB-per-day network logs. Then, a differential approach is employed to preprocess the extracted features so as to highlight the differences between normal and abnormal states. Third, those features are refined based on the feature importance we calculated. According to our experiment, the proposed feature extraction and refinement method can reduce computation without degrading the performance. Among the five types of failures, we achieve a 100% recall rate in four types and the rest can also reach 71%. Overall, the total average prediction accuracy of the proposed method is 94%. |
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Keywords: Core network, failure detection, route information, machine learning
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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