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dc.contributor.authorLi, Jien_US
dc.contributor.authorLiu, Haiyangen_US
dc.contributor.authorSollins, Karenen_US
dc.date.accessioned2023-03-29T14:43:10Z
dc.date.available2023-03-29T14:43:10Z
dc.date.issued2003-04
dc.identifier.urihttps://hdl.handle.net/1721.1/149324
dc.description.abstractPacket classification is a central function for a number of network applications, such as routing and firewalls. Most existing algorithms for packet classification scale poorly in either time or space when the database size grows. The scalable algorithm Aggregated Bit Vector (ABV) is an improvement on the Lucent bit vector scheme (BV), but has some limitations. Our algorithm, Aggregated and Folded Bit Vector (AFBV), seeks to reduce false matches while keeping the benefits of bit vector aggregation and avoiding rule rearrangement. It combines bit vector aggregation and folding to achieve this goal. Experiments showed that our algorithm outperforms both the BV and ABV schemes in synthetically generated databases.en_US
dc.relation.ispartofseriesMIT-LCS-TM-637
dc.titleScalable Packet Classification Using Bit Vector Aggregating and Foldingen_US


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