Automation of System Monitoring Based on Fuzzy Logic or Rules; Comparison of Two Designed Approaches with Regard to Computational Infrastructures
Identifieur interne : 000231 ( Main/Exploration ); précédent : 000230; suivant : 000232Automation of System Monitoring Based on Fuzzy Logic or Rules; Comparison of Two Designed Approaches with Regard to Computational Infrastructures
Auteurs : Włodzimierz Funika [Pologne] ; Filip Szura [Pologne] ; Jacek Kitowski [Pologne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2012.
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Abstract
Abstract: This paper is focused on monitoring automation of distributed systems. In the presented research, AI-based approaches to distributed monitoring related to large distributed systems such as grids, were explored. In both presented concepts knowledge is used to make decisions regarding management actions, using rules and fuzzy logic. The first concept is an agent-less rule-based solution, implemented in a high-level monitoring system called Saude-Net. It allows to define actions for monitored resources, using a kind of expert system. The second solution, which exploits agents and fuzzy logic, is realized in a system called SAMM Compliant Agent. Both presented systems are capable of reacting to observed failures and of modifying their knowledge to better fit possible problems with resources. We also present a short comparison of the two concepts, and an analysis of their usage.
Url:
DOI: 10.1007/978-3-642-28267-6_11
Affiliations:
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<front><div type="abstract" xml:lang="en">Abstract: This paper is focused on monitoring automation of distributed systems. In the presented research, AI-based approaches to distributed monitoring related to large distributed systems such as grids, were explored. In both presented concepts knowledge is used to make decisions regarding management actions, using rules and fuzzy logic. The first concept is an agent-less rule-based solution, implemented in a high-level monitoring system called Saude-Net. It allows to define actions for monitored resources, using a kind of expert system. The second solution, which exploits agents and fuzzy logic, is realized in a system called SAMM Compliant Agent. Both presented systems are capable of reacting to observed failures and of modifying their knowledge to better fit possible problems with resources. We also present a short comparison of the two concepts, and an analysis of their usage.</div>
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