Ants vs. faults: A swarm intelligence approach for diagnosing distributed computing networks

Abstract

Although much is known about the nature of testing structures for t-diagnosable systems, the problem of efficiently identifying the set of faulty units of a system in which the fault situation is known to be diagnosable remains an outstanding research issue. In this paper, we propose and evaluate an approach, based on swarm intelligence, to identify the set of faulty units in diagnosable systems. We consider t-diagnosable systems under the PMC model, where each node is capable of testing a particular subset of the other nodes in the system. We show that the ant-colony- based fault diagnosis algorithm is efficient, in that, it is able to diagnose a faulty situation in very short periods of time even if the number of faults is around the bound t, and with very few number of ants. The simulation results show that the new adaptive fault identification approach constitutes an addition to existing diagnosis algorithms.