TY - GEN
T1 - General ternary bit strings on commodity longest-prefix-match infrastructures
AU - Chuprikov, Pavel
AU - Kogan, Kirill
AU - Nikolenko, Sergey
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/21
Y1 - 2017/11/21
N2 - Ternary Content-Addressable Memory (tcam) is a powerful tool to represent network services with line-rate lookup time. There are various software-based approaches to represent multi-field packet classifiers. Unfortunately, all of them either require exponential memory or apply additional constraints on field representations (e.g, prefixes or exact values) to have line-rate lookup time. In this work, we propose alternatives to tcam and introduce a novel approach to represent packet classifiers based on ternary bit strings (without constraining field representation) on commodity longest-prefix-match (lpm) infrastructures. These representations are built on a novel property, prefix reorderability, that defines how to transform an ordered set of ternary bit strings to prefixes with lpm priorities in linear memory. Our results are supported by evaluations on large-scale packet classifiers with real parameters from ClassBench; moreover, we have developed a prototype in P4 to support these types of transformations.
AB - Ternary Content-Addressable Memory (tcam) is a powerful tool to represent network services with line-rate lookup time. There are various software-based approaches to represent multi-field packet classifiers. Unfortunately, all of them either require exponential memory or apply additional constraints on field representations (e.g, prefixes or exact values) to have line-rate lookup time. In this work, we propose alternatives to tcam and introduce a novel approach to represent packet classifiers based on ternary bit strings (without constraining field representation) on commodity longest-prefix-match (lpm) infrastructures. These representations are built on a novel property, prefix reorderability, that defines how to transform an ordered set of ternary bit strings to prefixes with lpm priorities in linear memory. Our results are supported by evaluations on large-scale packet classifiers with real parameters from ClassBench; moreover, we have developed a prototype in P4 to support these types of transformations.
UR - http://www.scopus.com/inward/record.url?scp=85041402002&partnerID=8YFLogxK
U2 - 10.1109/ICNP.2017.8117542
DO - 10.1109/ICNP.2017.8117542
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AN - SCOPUS:85041402002
T3 - Proceedings - International Conference on Network Protocols, ICNP
BT - 2017 IEEE 25th International Conference on Network Protocols, ICNP 2017
PB - IEEE Computer Society
T2 - 25th IEEE International Conference on Network Protocols, ICNP 2017
Y2 - 10 October 2017 through 13 October 2017
ER -