Approximate Packet Classifiers with Controlled Accuracy

Vitalii Demianiuk, Kirill Kogan, Sergey Nikolenko

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Article number9354448
Pages (from-to)1141-1154
Number of pages14
JournalIEEE/ACM Transactions on Networking
Volume29
Issue number3
DOIs
StatePublished - Jun 2021

Keywords

  • Software defined networking
  • quality of service

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