Compressing forwarding tables

Rottenstreich, Ori; Radan, Marat; Cassuto, Yuval; Keslassy, Isaac; Arad, Carmi; Mizrahi, Tal; Revah, Yoram; Hassidim, Avinatan

doi:10.1109/infcom.2013.6566915

Cited by 23 publications

(7 citation statements)

References 9 publications

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“…Compressing rules with prefix-only wildcards has a very old and rich literature in IPv4 networks. To mention a few, we can indicate to one-dimensional solutions ( Draves et al, 1999 ; Rétvári et al, 2013 ), a two-dimensional approach ( Rottenstreich et al, 2013 ), and multidimensional approaches like TCAM unification framework ( Norige, Liu & Torng, 2013 ) and TCAM Razor ( Liu, Meiners & Torng, 2009 ). Further references could be found in Braun & Menth (2014a) .…”

Section: Related Workmentioning

confidence: 99%

WildMinnie: compression of software-defined networking (SDN) rules with wildcard patterns

Khanmirza¹

2022

PeerJ Computer Science

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Software-defined networking (SDN) enables fast service innovations through network programmability. In SDN, a logically centralized controller compiles a set of policies into the network-level rules. These rules are inserted in the TCAM memory of SDN-enabled switches enabling high-speed matching and forwarding of packets. Unfortunately, TCAMs are available in limited capacities and fall short of accommodating all intended rules, especially in networks with large distinct flows like datacenters. Rule compression is a technique that reduces the number of rules by aggregating them with some similarity factors. This paper introduces WildMinnie, a new rule compression method that aggregates rules based on their common address non-prefix wildcards derived from a group of rules with the same output port number. We explore rule conflict issues and provide solutions to resolve them. We demonstrate the capability of WildMinnie in various datacenter topologies with traffics having different diversity of source-destination addresses and show that WildMinnie outperforms the best-known compression method by 20%, on average.

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Section: Related Workmentioning

confidence: 99%

WildMinnie: compression of software-defined networking (SDN) rules with wildcard patterns

Khanmirza¹

2022

PeerJ Computer Science

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“…Although TCAM enables parallel lookups on rules for line‐speed classification, this brutal force hardware solution is not only expensive, but also very power‐hungry, which seriously limits its scalability. During the past decade, a lot of methods and algorithms had been proposed to alleviate these problems, such as classifier minimisation [2–6], range encoding [7–11] and pre‐classifier [12–15]. However, due to inherent limitations of TCAM, the TCAM capacity is not expected to increase significantly in the near future [4].…”

Section: Introductionmentioning

confidence: 99%

Memory‐efficient recursive scheme for multi‐field packet classification

Bai

et al. 2019

IET Communications

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“…Accordingly, we say that the obtained success probability for this encoding scheme C D = (σ 1 , σ 2 ) is the sum of probabilities for the successfully-encoded entries, i.e. Recently, an encoding scheme that minimizes the maximal encoding width of a given set of data entries was suggested ( in [3]. Unfortunately, the fixed width of a memory array is typically predetermined by its manufacturer and cannot be changed dynamically by the user.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the fixed width of a memory array is typically predetermined by its manufacturer and cannot be changed dynamically by the user. Furthermore, the worst-case approach of [3] may be too stringent. Therefore, the new probabilistic model we consider here of maximizing the success probability for a fixed L is a more realistic one.…”

Section: Introductionmentioning

confidence: 99%

Compression for fixed-width memories

Rottenstreich

Berman

Cassuto

et al. 2013

2013 IEEE International Symposium on Information Theory

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To enable direct access to a memory word based on its index, memories make use of fixed-width arrays, in which a fixed number of bits is allocated for the representation of each data entry. In this paper we consider the problem of encoding data entries of two fields, drawn independently according to known and generally different distributions. Our goal is to find two prefix codes for the two fields, that jointly maximize the probability that the total length of an encoded data entry is within a fixed given width. We study this probability and develop upper and lower bounds. We also show how to find an optimal code for the second field given a fixed code for the first field.

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Compressing forwarding tables

Cited by 23 publications

References 9 publications

WildMinnie: compression of software-defined networking (SDN) rules with wildcard patterns

WildMinnie: compression of software-defined networking (SDN) rules with wildcard patterns

Memory‐efficient recursive scheme for multi‐field packet classification

Compression for fixed-width memories

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