Scalable Packet Classification Using Bit Vector Aggregating and Folding

Abstract

Packet 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.