Root Cause Analysis of Network Failures Using Machine Learning and Summarization Techniques
Root cause analysis includes the methods to identify the sources of errors in a network. Most techniques rely on knowledge models of the system, which are usually built by using network operators' expertise. This presents problems related to knowledge extraction, scalability, and understandabil...
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| Veröffentlicht in: | IEEE communications magazine 2017-09, Vol.55 (9), p.126-131 |
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| creator | Gonzalez, Jose Manuel Navarro Jimenez, Javier Andion Lopez, Juan Carlos Duenas Parada G, Hugo A. |
| description | Root cause analysis includes the methods to identify the sources of errors in a network. Most techniques rely on knowledge models of the system, which are usually built by using network operators' expertise. This presents problems related to knowledge extraction, scalability, and understandability. We propose an offline method based on machine learning techniques for the automatic identification of dependencies between system events, enhanced with summarization, operations on graphs, and visualization that help network operators identify the root causes of errors. We illustrate it with examples from a corporate network. |
| doi_str_mv | 10.1109/MCOM.2017.1700066 |
| format | Magazinearticle |
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| source | IEEE Electronic Library (IEL) |
| subjects | Artificial intelligence Data mining Failure analysis Identification methods Knowledge based systems Knowledge engineering Machine learning Machine learning algorithms Operators Predictive models Root cause analysis |
| title | Root Cause Analysis of Network Failures Using Machine Learning and Summarization Techniques |
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