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dc.contributor.authorWang, P.C.en_US
dc.description.abstractIn next-generation networks, packet classification is used to categorize incoming packets into multiple forwarding classes based on pre-defined filters and make information accessible for quality of service or security handling in the network. To pursue better search performance, numerous algorithms of packet classification have been proposed and optimized for certain filter databases; however, these algorithms may not scale in either storage or speed performance or both for the filter databases with different characteristics. This paper presents an efficient algorithm by combining two complementary algorithms, Cross-producting and Pruned Tuple Space Search, to make packet classification both fast and scalable. Unlike the existing algorithms whose performance is contingent on the filter database attributes, our algorithm shows better scalability and feasibility. We evaluate the performance of our scheme with filter databases of varying sizes and characteristics. The experimental results demonstrate that the new algorithm improves the speed and storage performance simultaneously. We also introduce the procedure of incremental updates. Copyright (C) 2009 John Wiley & Sons, Ltd.en_US
dc.relationInternational Journal of Communication Systemsen_US
dc.relation.ispartofseriesInternational Journal of Communication Systems, Volume 23, Issue 6-7, Page(s) 841-860.en_US
dc.subjectpacket classificationen_US
dc.subjectnext-generation networksen_US
dc.subjectnetwork securityen_US
dc.subjectnetwork intrusion detection systemsen_US
dc.titleScalable packet classification using a compound algorithmen_US
dc.typeJournal Articlezh_TW
Appears in Collections:資訊網路與多媒體研究所


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