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Packet classification is important for applications such as firewalls, intrusion detection, and differentiated services. Existing algorithms for packet classification reported in the literature scale poorly in either time or space as filter databases grow in size. Hardware solutions such as TCAMs do not scale to large classifiers. However, even for large classifiers (say 100,000 rules), any packet is likely to match a few (say 10) rules. Our paper seeks to exploit this observation to produce adoi:10.1145/383059.383075 dblp:conf/sigcomm/BaboescuV01 fatcat:euaxk2hvwva2hg2bjqonupucme