Fault identification and diagnosis using greedy Back Tracking approach in large-scale network

Abstract

In this paper, we discuss the problems in large scale networks failure information. For this We propose a fault identification system initiate by an application when the application identify the incidence of a failure, in order to realize supervision systems that automatically find the foundation of a failure. In existing detection systems, there are three problems for constructing self managing applications: i) the detection results are not sent to the applications, ii) they cannot identify the source failure from all of the detected failures, and iii) configuring the detection system for networked system is hard work. To overcoming these problems, the proposed system takes three approaches: i) the system receives failure information from an application and returns a result set to the application, ii) the system identifies the source failure using greedy technique, and iii) the system obtains information of the monitored system from a database. The error relationship is expressed by a tree. This tree is called greedy error tree. The database provides information which is system entities such as hardware devices, software object, and network topology. When the proposed system starts looking for the source of a failure, causal relations from a greedy error relation tree are referred to, and the correspondence of error definitions and actual objects is derived using the database. We show the design of the identification operation activated by the failure information and the architecture of the proposed system

Authors and Affiliations

U. Sridhar , Dr. G. Gunasekaran

Keywords

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  • EP ID EP87789
  • DOI -
  • Views 129
  • Downloads 0

How To Cite

U. Sridhar, Dr. G. Gunasekaran (2013). Fault identification and diagnosis using greedy Back Tracking approach in large-scale network. International Journal of Advanced Research in Computer Engineering & Technology(IJARCET), 2(1), 37-41. https://www.europub.co.uk/articles/-A-87789