SciELO - Scientific Electronic Library Online

 
vol.16 issue2Combining Classifiers for BioinformaticsInferring Market Strategies: Applying Data-Mining to Analysis of Financial Markets author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

On-line version ISSN 2007-9737Print version ISSN 1405-5546

Abstract

FALCON, Rafael; ALMEIDA, Marcio; NAYAK, Amiya  and  BELLO, Rafael. System-Level Fault Diagnosis with Dynamic Mesh Optimization. Comp. y Sist. [online]. 2012, vol.16, n.2, pp.203-220. ISSN 2007-9737.

The efficient identification of hardware and software faults in parallel and distributed systems still remains a challenge in today's most prolific decentralized environments. System-level fault diagnosis is concerned with the detection of all faulty nodes in a set of hundreds (or even thousands) of interconnected units. This is accomplished by thoroughly examining the collection of outcomes of all tests carried out by the nodes under a particular test model. Such task has non-polynomial complexity and can be posed as a combinatorial optimization problem. In this paper we employ Dynamic Mesh Optimization (DMO) to detect faulty units in diagnosable systems. The proposed method encodes the potential solutions as binary vectors and exploits problem-specific knowledge to cope with infeasible individuals. The empirical analysis confirms that the DMO-based scheme outperforms existing techniques in terms of convergence speed and memory requirements, thus becoming a viable approach for real-time fault diagnosis in large-size systems.

Keywords : Fault diagnosis; input syndrome; dynamic mesh optimization; invalidation model,; comparison model.

        · abstract in Spanish     · text in English     · English ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License