Performing exact computations can require significant resources. Approximate computing allows to alleviate resource constraints, sacrificing the accuracy of results. In this work, we consider a generalization of the classical packet classification problem . Our major contribution is to introduce representations of approximate packet classifiers with controlled accuracy and optimization techniques to reduce classifier sizes exploiting this new level of flexibility. In this work, we propose methods constructing efficient approximate representations for both LPM (longest prefix match) classifiers and classifiers with general ternary-bit filters. We validate our theoretical results with a comprehensive evaluation study showing that a small error in the actions of a classifier can lead to significant memory reductions, often comparable to the best possible theoretical reduction in the trivial case when all rules have the same action.

中文翻译：

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具有可控精度的近似数据包分类器

执行精确计算可能需要大量资源。 近似计算允许缓解资源限制，牺牲结果的准确性。在这项工作中，我们考虑了经典的推广数据包分类问题 . 我们的主要贡献是介绍了近似分组分类器控制精度和优化技术，以利用这种新的灵活性水平来减少分类器的大小。在这项工作中，我们提出了为 LPM（最长前缀匹配）分类器和具有通用三元位滤波器的分类器构建有效近似表示的方法。我们通过综合评估研究验证了我们的理论结果，表明分类器动作中的一个小错误可以导致显着的记忆减少，通常与所有规则具有相同动作的琐碎情况下的最佳理论减少相当。