TurboTSS: A Packet Classifier with Fast Rule Lookup and Update for the Cloud

摘要

Software packet classifiers are essential components in cloud environments, where they must balance fast rule lookup, rapid rule updates, and minimal memory overhead. Existing approaches—such as decision tree-based and tuple space-based methods—typically prioritize one performance metric at the expense of the others. We propose TurboTSS, a hybrid solution that achieves the fastest rule update speed in tuple-space-based solutions, and incorporates four key enhancements to accelerate rule lookup: selecting per-rule significant fields to build tuples, using tries to filter unnecessary tuples, using cross-field trees to fetch rules within a tuple, and using extra storage to lookup big rules. Our evaluations on the TurboTSS prototype show that TurboTSS provides the fastest rule lookup speed and moderate-fast rule update speed, sacrificing only some extra memory space, compared with state-of-the-art solutions.