SAKURA a Model Based Root Cause Analysis Framework for vIMS

Sihem Cherrared 1 Sofiane Imadali 1 Eric Fabre 2 Gregor Gössler 3
2 SUMO - SUpervision of large MOdular and distributed systems
Inria Rennes – Bretagne Atlantique , IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL
3 SPADES - Sound Programming of Adaptive Dependable Embedded Systems
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : 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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Sihem Cherrared, Sofiane Imadali, Eric Fabre, Gregor Gössler. SAKURA a Model Based Root Cause Analysis Framework for vIMS. MobiSys 2019 - 17th ACM International Conference on Mobile Systems, Applications, and Services, Jun 2019, Seoul, South Korea. ACM Press, pp.594-595. ⟨hal-02291163⟩

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