Section: New Results

Specific studies: network and service diagnosis

Participants : Eric Fabre, Carole Hounkonnou.

This work represents part of our activities within the research group “High Manageability,” supported by the common lab of Alcatel-Lucent Bell Labs (ALBLF) and INRIA. It is also supported by the UniverSelf EU integrated project, and conducted in relation with Orange Labs.

The objective is to develop a framework for the joint diagnosis of networks and of the supported services. We are aiming at a model-based approach, in order to taylor the methods to a given network instance and to follow its evolution. We also aim at active diagnosis methods, that collect and reason on alarms provided by the network, but that can also trigger tests or the collection of new observations in order to refine a current diagnosis.

In 2011, the main effort was dedicated to a key and difficult part of this approach: the definition of a methodology for self-modelling. This consists in automatically building a model of the monitored system, by instantiating generic network elements. There are several difficulties to address:

• The model must capture several layers, from the physical architecture up to the service architecture and its protocols. As a case-study, we have chosen VoIP services on an IMS network, deployed over a wired IP network.

• The model should be hierarchical, to allow for multiscale reasoning, and to reflect the intrinsic hierarchical nature of the managed network.

• The model should be generic, i.e. obtained by assembling component instances coming from a reduced set of patterns, just like a text is obtained by assembling words.

• The model should be adaptive, to capture the evolving part of the network (e.g. introduction of new elements) but also its intrinsically dynamic nature (e.g. opened/closed connections).

• The model should display the hierarchical dependency of resources, specifically the fact that lower-level resources are assembled to provide a support to a higher level resource or functionality.

• The model should allow progressive discovery and refinement: for a matter of size, it is not possible to first build a model of the complete network and then monitor it; one must adopt an approach where the model is build on-line, and where the construction is guided by the progress of the diagnosis algorithms.

The first elements of a methodology achieving these objectives have been designed in 2011. The next efforts will aim at refining the grammar of this model, for our specific case study, and at developing the dedicated diagnosis algorithms. For the latter, we envision a new setting of hierarchical and generic Bayesian networks, in order to capture the dependencies between network elements at different granularities.