Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33000
Title: Detection of fractal breakdowns by the novel real-time pattern detection model (enhanced-RTPD+holder exponent) for web applications
Authors: Dillon, TS
Lin, WWK
Chang, E
Wong, AKY
Keywords: Internet
Fractals
Pattern classification
Telecommunication traffic
Time series
Issue Date: 2007
Publisher: IEEE
Source: 10th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing, 2007 : ISORC '07, 7-9 May 2007, Santorini Island, p. 79-86 (Invited Paper) How to cite?
Abstract: The M3RT-based real-time traffic pattern detector proposed identifies the Internet traffic pattern on the fly. Firstly it determines if a time series aggregate is stationary. Secondly it confirms if the aggregate exhibits short-range dependence (SRD) or long-range dependence (LRD). Thirdly it detects if the smooth system operation has suddenly become irregular and chaotic. This detection is achieved by computing the instantaneous value of the Holder exponent that has a (0,1) range to accommodate different degrees of fractality. When the Holder exponent has wandered outside the (0,1) region, fractal breakdown has occurred. The capability of detecting such breakdowns by a real-time application enables it to avoid sudden failure. The Intel's VTune Performance Analyzer indicates the proposed model can be deployed in real time effectively. This feature is of importance to the reliability improvement of Web applications which run on the Internet
URI: http://hdl.handle.net/10397/33000
ISBN: 0-7695-2765-5
DOI: 10.1109/ISORC.2007.29
Appears in Collections:Conference Paper

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