A novel artificial-immune-based approach for system-level fault diagnosis

Abstract

The problem of self-diagnosis of multiprocessor and multicomputer systems under the generalized comparison model (GCM) is considered. GCM assumes that a set of jobs is assigned to pairs of units and that the outcomes are compared by the units themselves (self-diagnosis). Based on the set of comparison outcomes (agreements and disagreements among the units), the set of up to t faulty nodes is identified (t-diagnosable systems). This paper proposes an artificial-immune-based algorithm to solve the fault identification problem. The immune diagnosis algorithm correctly identifies the set of faulty units, and it has been evaluated using randomly generated t-diagnosable systems. Simulation results indicate that the proposed approach is a viable alternative to solve the GCM-based diagnosis problem.