Journal Article

A neural network for diagnosing multiprocessor and multicomputer systems

Elhadef, Mourad

2009

Overview

Purpose - The purpose of this paper is to describe a novel diagnosis approach, using neural networks (NNs), which can be used to identify faulty nodes in distributed and multiprocessor systems.Design methodology approach - Based on a literature-based study focusing on research methodology and theoretical frameworks, the conduct of an ethnographic case study is described in detail. A discussion of the reporting and analysis of the data is also included.Findings - This work shows that NNs can be used to implement a more efficient and adaptable approach for diagnosing faulty nodes in distributed systems. Simulations results indicate that the perceptron-based diagnosis is a viable addition to present diagnosis problems.Research limitations implications - This paper presents a solution for the asymmetric comparison model. For a more generalized approach that can be used for other comparison or invalidation models this approach requires a multilayer neural network.Practical implications - The extensive simulations conducted clearly showed that the perceptron-based diagnosis algorithm correctly identified all the millions of faulty situations tested. In addition, the perceptron-based diagnosis requires an off-line learning phase which does not have an impact on the diagnosis latency. This means that a fault set can be easily and rapidly identified. Simulations results showed that only few milliseconds are required to diagnose a system, hence, one can start talking about \"real-time\" diagnosis.Originality value - The paper is first work that uses NNs to solve the system-level diagnosis problem.

• Publisher: Emerald Group Publishing Limited
• Subject: Algorithms / Communication / Diagnostic testing / Diagnostics / Fault diagnosis / Fault tolerance / Mathematics / Networks / Neural nets / Neural networks / Studies / Systems analysis / Topology