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Summary: Using Online Traffic Statistical Matching for
Optimizing Packet Filtering Performance
Adel El-Atawy, Taghrid Samak, Ehab Al-Shaer
School of Computer Science, DePaul University
Chicago, USA
{aelatawy,taghrid,ehab}@cs.depaul.edu
Hong Li
Intel
hong.c.li@intel.com
Abstract-- Packet classification plays a critical role in many of
the current networking technologies, and efficient yet lightweight
packet classification techniques are highly crucial for their
successful deployment. Most of the current packet classification
techniques exploit the characteristics of classification policies,
without considering the traffic behavior in optimizing their search
data structures. In this paper, we present novel techniques that
utilize traffic characteristics coupled with careful analysis of the
policy to obtain adaptive methods that can accommodate varying
traffic statistics while maintaining a high throughput. The first
technique uses segmentation of the traffic space to achieve
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