Detecting and predicting outages in mobile networks with log data
| Autor: | Chitra Phadke, Eric Falk, Radu State, Vijay K. Gurbani, Dan Kushnir, Veena B. Mendiratta |
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| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science Reliability (computer networking) Mobile computing 020206 networking & telecommunications 02 engineering and technology computer.software_genre Mixture model Telecommunications network 0202 electrical engineering electronic engineering information engineering Cellular network 020201 artificial intelligence & image processing Network performance Anomaly detection Data mining Mobile telephony business computer |
| Zdroj: | ICC |
| DOI: | 10.1109/icc.2017.7996706 |
| Popis: | Modern cellular networks are complex systems offering a wide range of services and present challenges in detecting anomalous events when they do occur. The networks are engineered for high reliability and, hence, the data from these networks is predominantly normal with a small proportion being anomalous. From an operations perspective, it is important to detect these anomalies in a timely manner, to correct vulnerabilities in the network and preclude the occurrence of major failure events. The objective of our work is anomaly detection in cellular networks in near real-time to improve network performance and reliability. We use performance data from a 4G LTE network to develop a methodology for anomaly detection in such networks. Two rigorous prediction models are proposed: a non-parametric approach (Chi-Square test), and a parametric one (Gaussian Mixture Models). These models are trained to detect differences between distributions to classify a target distribution as belonging to a normal period or abnormal period with high accuracy. We discuss the merits between the approaches and show that both provide a more nuanced view of the network than simple thresh-olds of success/failure used by operators in production networks today. |
| Databáze: | OpenAIRE |
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