Replication free rule grouping for packet classification

Abstract

Most recent works demonstrate that grouping methodology could bring significant reduction of memory usage to decision-tree packet classification algorithms, with insignificant impact on throughput. However, these grouping techniques can hardly eliminate rule-replication completely. This work proposes a novel rule grouping algorithm without any replication. At each space decomposition step, all rules projecting on the split dimension form the maximum number of non-overlapped ranges, which guarantees the modest memory usage and grouping speed. Evaluation shows that the proposed algorithm achieves comparable memory size with less pre-processing time.