Acceleration of Packet Classification Using Adjacency List of Rules

Packet classification is used to determine the behavior of packets incoming to network devices. Since it is achieved using linear search on a classification rule list, a large number of rules leads to longer communication latency. To decrease this latency, the problem is generalized as optimal rule ordering (ORO), which aims to identify the order of rules that minimizes the classification latency caused by packet classification while preserving the classification policy. Since ORO is known to be NP-complete, various heuristics for ORO have been proposed. Sub-graph merging (SGM) is the state-of-the-art heuristic algorithm for ORO. However, the SGM algorithm does not terminate in most cases because of inappropriate updates of the array that stores the number of reachable rules. Moreover, since SGM uses the adjacent matrix to maintain the preceding relation on the rules, a considerable amount of time is consumed when the preceding relation is complex. In this paper, we propose a correction for SGM that can terminate for any instance. The proposed algorithm decreases reordering time by about 99\% compared to SGM. Furthermore, we propose a reordering algorithm that selects sub-graphs more comprehensively than SGM. Doing so decreases the latency in comparison with SGM. We show the efficiency of the algorithms through experiments.