SAKURA a Model Based Root Cause Analysis Framework for vIMS
Overview & Synopsis
Model based machine learning (MBML) techniques solve novel diagnosis problems and provide explanations for their decisions. However, current MBMLs suffer some limitations, since virtualization of network brings new challenges such as the dynamic topology and elasticity. Those limitations include the high dependency on previous knowledge and the difficulty to represent the model. To face those limitations, we propose SAKURA: a root cause analysis framework for the virtual Ip Multimedia Subsystem (vIMS). SAKURA is composed of a self-modeling and a constraints solver algorithm.
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hal.science)
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Document Information
| Document Title | SAKURA a Model Based Root Cause Analysis Framework for vIMS |
|---|---|
| Author / Organization | Sihem Cherrared, Sofiane Imadali, Eric Fabre, Gregor Gössler |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2019 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-213944 |
| Original Source Link | Visit Source Page on hal.science » |