Parallel packet classification via policy table pre-partitioning

Most existing packet classification algorithms are decision-tree-based, which are easy-implemented and have reasonably good performance. However, due to rule duplication caused by rule overlapping, these solutions always occupy large and indeterministic-sized memory space, making them only be implemented with mass-but-slow SDRAMs and incompetent for future high-end applications. In this paper, by proposing a novel pre-partitioning scheme for the rule set, we eliminate the overlap among the rules and, therefore, avoid rule duplications. We then further develop a parallel packet classification scheme and a quarter-cut decision tree algorithm. The large rule set is distributedly stored in multiple search engines and the classification can be performed in parallel, independently. Much smaller and more deterministic memory requirement is achieved, as well as O(Log(N)) processing delays. Experimental results show that for a policy table with over 1500 rules, only 36 Kbytes memory is required, and averagely 12 memory accesses are needed for each classification.