PCL: Packet Classification with Limited Knowledge

Abstract

We introduce a novel representation of packet classifiers allowing to operate on partially available input data varying dynamically. For a given packet classifier, availability of fields or complexity of field computations, and free target specific resources, the proposed infrastructure computes a classifier representation satisfying performance and robustness requirements. We show the feasibility to reconstruct a classification result in this noisy environment, allowing for the improvement of performance and the achievement of additional robustness levels of network infrastructure. Our results are supported by extensive evaluations in various settings where only a partial input is available.