Multiple decision-tree packet classification algorithm based on rule set partitioning

Abstract

For solving the problem of decision-tree algorithms' too much memory usage when coping with packet classification under the circumstance of high rate network and large volume rule set, a multiple decision-tree algorithm based on rule set partitioning was proposed in this paper. On the condition of controlling the number of subsets, heuristics were used to partition the rule set into limited number of subsets, in which the overlapping rules had been separated. Cascading decisiontree structure was proposed to lower the depth and reduce search time. The theoretical analysis shows that space complexity has been reduced greatly compared to traditional single decision-tree algorithm. The simulation results demonstrate that the algorithm reduces memory usage about 30% and has better dimension scalability when being compared with EffiCuts, which has the best performance for memory usage so far.