NDLI: Failure Prediction based on multi-parameter analysis in support of autonomic networks

Content Provider	IEEE Xplore Digital Library
Author	Abed, H.J. Al-Fuqaha, A. Aljaafreh, A.
Copyright Year	2011
Description	Author affiliation: Computer Science Department, Western Michigan University, USA (Abed, H.J.; Al-Fuqaha, A.; Aljaafreh, A.)
Abstract	In this paper, we present a Failure Prediction System (FPS) using a novel algorithm that extracts frequent anomalous behaviors based on multi-scale trend analysis of multiple network parameters. The proposed Correlation Analysis Across Parameters algorithm (CAAP) utilizes multiple levels of timescale analysis to reveal the frequent anomalous behaviors. The CAAP philosophy is that failures usually do not occur because of change in a single parameter behavior; instead, a set of interrelated parameters change their behaviors jointly and lead to a particular failure. The proposed algorithm requires an enhanced version of FABM algorithm which was presented by the authors in a previous paper and was used to analyze each parameter's behavior individually. Moreover, the new version, called FABMG algorithm, has the same polynomial computational complexity of $O(n^{2}).$ The CAAP utilizes the data mining techniques of association rules mining in order to reveal the existed correlation relationships. Consequently, as found in this work, this approach improves the quality of the FPS results which was relying on individual parameter analysis only. One of the strengths of CAAP is that it requires the FABMG output only, i.e. it does not require rescanning the database in order to produce the correlation results.
Starting Page	77
Ending Page	81
File Size	318737
Page Count	5
File Format	PDF
ISBN	9781457704017
e-ISBN	9781457704024
DOI	10.1109/ICCITECHNOL.2011.5762698
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-29
Publisher Place	Jordan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Correlation Databases Frequent anomalous behaviors Prediction algorithms Failure prediction Complexity theory Association rules Time-scale analysis Autonomic network management
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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