SAKURA a Model Based Root Cause Analysis Framework for vIMS

International audienceModel 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