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SFC Self-Modeling and Active Diagnosis

Sihem Cherrared 1, 2 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 : The 5G system should be flexible to cope with diversity of functions and infrastructure, in an efficient way while supporting a wide range of services associated with a set of requirements through Service Function Chaining (SFC). Fault management plays a paramount role to fulfill the SFC requirements towards reliability of telecommunication networks. Model-based (MB) approaches are knowledge-based systems that reason about a system from an explicit representation of its structure and functional behavior through a dependency model. MB techniques solve novel diagnosis problems and provide explanations for their decisions. However, current MB methods suffer from some limitations due to network virtualization challenges: lack of network visibility and dynamic topologies. To tackle those limitations, we propose a self-modeling approach and an active diagnosis process for virtual networks that considers two types of knowledge to build the model: acquired knowledge and learned knowledge provided by fault injection to expand and validate the proposed model. The experimental results from their application to a real-world virtual IP Multimedia Subsystem (vIMS) use case show that the self-modeling and the active diagnosis procedures are effective in determining the root cause(s) of a failure and explaining fault propagation.
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https://hal.inria.fr/hal-03352706
Contributor : Gregor Gössler Connect in order to contact the contributor
Submitted on : Thursday, September 23, 2021 - 2:23:28 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM

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Sihem Cherrared, Sofiane Imadali, Eric Fabre, Gregor Gössler. SFC Self-Modeling and Active Diagnosis. IEEE Transactions on Network and Service Management, 2021, 18 (3), pp.2515-2530. ⟨10.1109/TNSM.2021.3086424⟩. ⟨hal-03352706⟩

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