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Proceedings of the Institute for System Programming of the RAS, 2021, Volume 33, Issue 4, Pages 77–86
DOI: https://doi.org/10.15514/ISPRAS-2021-33(4)-6
(Mi tisp614)
 

Data compression algorithms for flow tables in network processor RuNPU

N. I. Nikiforov, D. Yu. Volkanov

Lomonosov Moscow State University
Abstract: This paper addresses the problem of packet classification within a network processor (NP) architecture without the separate associative device. By the classification, we mean the process of identifying a packet by its header. The classification stage requires the implementation of data structures to store the flow tables. In our work, we consider the NP without the associative memory. Flow tables are represented by an assembly language program in the NP. For translating flow tables into assembly language programs, a tables translator was used. The main reason for implementing data compression algorithms in a flow tables translator is that modern flow tables can take up to tens of megabytes. In this paper, we describe the following data compression algorithms: Optimal rule caching, recursive end-point cutting and common data compression algorithms. An evaluation of the implemented data compression algorithms was performed on a simulation model of the NP.
Keywords: network processor, software-defined networks, packet classification, data compression.
Funding agency Grant number
Russian Foundation for Basic Research 19-07-01076
This work is partially supported by the Russian Foundation for Basic Research under grant 19-07-01076
Document Type: Article
Language: English
Citation: N. I. Nikiforov, D. Yu. Volkanov, “Data compression algorithms for flow tables in network processor RuNPU”, Proceedings of ISP RAS, 33:4 (2021), 77–86
Citation in format AMSBIB
\Bibitem{NikVol21}
\by N.~I.~Nikiforov, D.~Yu.~Volkanov
\paper Data compression algorithms for flow tables in network processor RuNPU
\jour Proceedings of ISP RAS
\yr 2021
\vol 33
\issue 4
\pages 77--86
\mathnet{http://mi.mathnet.ru/tisp614}
\crossref{https://doi.org/10.15514/ISPRAS-2021-33(4)-6}
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