Distributed Consistency-Based Diagnosis

Authors:
Vincent Armant;Philippe Dague;Laurent Simon
Affiliations:
LRI, Univ. Paris-Sud 11, CNRS and INRIA Saclay, Orsay Cedex, France 91893;LRI, Univ. Paris-Sud 11, CNRS and INRIA Saclay, Orsay Cedex, France 91893;LRI, Univ. Paris-Sud 11, CNRS and INRIA Saclay, Orsay Cedex, France 91893
Venue:
LPAR '08 Proceedings of the 15th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning
Year:
2008

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Abstract

A lot of methods exist to prevent errors and incorrect behaviors in a distributed framework, where all peers work together for the same purpose, under the same protocol. For instance, one may limit them by replication of data and processes among the network. However, with the emergence of web services, the willing for privacy, and the constant growth of data size, such a solution may not be applicable. For some problems, failure of a peer has to be detected and located by the whole system. In this paper, we propose an approach to diagnose abnormal behaviors of the whole system by extending the well known consistency-based diagnosis framework to a fully distributed inference system, where each peer only knows the existence of its neighbors. Contrasting with previous works on model-based diagnosis, our approach computes all minimal diagnoses in an incremental way, without needs to get any conflict first.