Skip to Main Content
Packet classification is central to a wide array of Internet applications and services, with its approaches mostly involving either hardware support or optimization steps needed by software-oriented techniques (to add precomputed markers and insert rules in the search data structures). Unfortunately, an approach with hardware support is expensive and has limited scalability, whereas one with optimization fails to handle incremental rule updates effectively. This work deals with rapid packet classification, realized by hashing round-down prefixes (HaRP) in a way that the source and the destination IP prefixes specified in a rule are rounded down to “designated prefix lengths” (DPL) for indexing into hash sets. HaRP exhibits superb hash storage utilization, able to not only outperform those earlier software-oriented classification techniques but also well accommodate dynamic creation and deletion of rules. HaRP makes it possible to hold all its search data structures in the local cache of each core within a contemporary processor, dramatically elevating its classification performance. Empirical results measured on an AMD 4-way 2.8 GHz Opteron system (with 1 MB cache for each core) under six filter data sets (each with up to 30 K rules) obtained from a public source unveil that HaRP enjoys up to some 3.6× throughput level achievable by the best known decision tree-based counterpart, HyperCuts (HC).