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Data Mining Meets Performance Evaluation: Fast Algorithms for Modeling Bursty Traffic

10 years 5 months ago
Data Mining Meets Performance Evaluation: Fast Algorithms for Modeling Bursty Traffic
Network, web, and disk I/O traffic are usually bursty, self-similar [9, 3, 5, 6] and therefore can not be modeled adequately with Poisson arrivals[9]. However, we do want to model these types of traffic and to generate realistic traces, because of obvious applications for disk scheduling, network management, web server design. Previous models (like fractional Brownian motion, FARIMA etc) tried to capture the `burstiness'. However, the proposed models either require too many parameters to fit and/or require prohibitively large (quadratic) time to generate large traces. We propose a simple, parsimonious method, the -model , which solves both problems: It requires just one parameter, and it can easily generate large traces. In addition, it has many more attractive properties: (a) With our proposed estimation algorithm, it requires just a single pass over the actual trace to estimate . For example, a one-day-long disk trace in milliseconds contains about 86Mb data points and requires...
Mengzhi Wang, Ngai Hang Chan, Spiros Papadimitriou
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2002
Where ICDE
Authors Mengzhi Wang, Ngai Hang Chan, Spiros Papadimitriou, Christos Faloutsos, Tara M. Madhyastha
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