Abstract
This paper makes two contributions: (i) it presents a scheme for classifying and identifying Internet traffic flows which carry a large number of packets (or bytes) and are persistent in nature (also known as the elephants), from flows which carry a small number of packets (or bytes) and die out fast (commonly referred to as the mice), and (ii) illustrates how non-parametric Parzen window technique can be used to construct the probability density function (pdf) of the elephants present in the original traffic stream. We validate our approach using a 15-minute trace containing around 23 million packets from NLANR.
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Keywords
- Bloom Filter
- Traffic Stream
- Integrate Mean Square Error
- Gaussian Kernel Function
- Asymptotic Equipartition Property
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Kundu, S.R., Pal, S., Basu, K., Das, S.K. (2007). Fast Classification and Estimation of Internet Traffic Flows. In: Uhlig, S., Papagiannaki, K., Bonaventure, O. (eds) Passive and Active Network Measurement. PAM 2007. Lecture Notes in Computer Science, vol 4427. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71617-4_16
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DOI: https://doi.org/10.1007/978-3-540-71617-4_16
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