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- W4232623607 abstract "Several range reencoding schemes have been proposed to mitigate the effect of range expansion and the limitations of small capacity, large power consumption, and high heat generation of TCAM-based packet classification systems. However, they all disregard the semantics of classifiers and therefore miss significant opportunities for space compression. In this paper, we propose new approaches to range reencoding by taking into account classifier semantics. Fundamentally different from prior work, we view reencoding as a topological transformation process from one colored hyperrectangle to another where the color is the decision associated with a given packet. We present two orthogonal, yet composable, reencoding approaches, domain compression and prefix alignment. Our techniques significantly outperform all previous reencoding techniques. In comparison with the state-of-the-art results, our experimental results show that our techniques achieve at least 7 times more space reduction in terms of TCAM space for an encoded classifier and at least 3 times more space reduction in terms of TCAM space for a reencoded classifier and its transformers." @default.
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- W4232623607 date "2009-06-15" @default.
- W4232623607 modified "2023-09-26" @default.
- W4232623607 title "Topological transformation approaches to optimizing TCAM-based packet classification systems" @default.
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- W4232623607 doi "https://doi.org/10.1145/2492101.1555359" @default.
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