Compressing IP forwarding tables for fun and profit

Rétvári, Gábor; Csernátony, Zoltán; Kő̈rösi, Attila; Tapolcai, János; Császár, András; Enyedi, Gábor; Pongrácz, Gergely

doi:10.1145/2390231.2390232

Cited by 6 publications

(8 citation statements)

References 15 publications

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“…We associate a fixed cost α with any such change of a single rule in the FIB. Note that we represent the update cost as a constant to keep the model general: α is not specific for any particular FIB data structure (e.g., trie, cache, or Multibit Burrows-Wheeler [12]), but may also model the cost of transmitting a control packet between an SDN controller and the OpenFlow switch. (See also [7].)…”

Section: The Modelmentioning

confidence: 99%

Competitive FIB Aggregation for Independent Prefixes: Online Ski Rental on the Trie

Bieńkowski

Schmid

2013

Lecture Notes in Computer Science

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Abstract. This paper presents an asymptotically optimal online algorithm for compressing the Forwarding Information Base (FIB) of a router under a stream of updates (namely insert rule, delete rule, and change port of prefix). The objective of the algorithm is to dynamically aggregate forwarding rules into a smaller but equivalent set of rules while taking into account FIB update costs. The problem can be regarded as a new variant of ski rental on the FIB trie, and we prove that our deterministic algorithm is 3.603-competitive. Moreover, a lower bound of 1.636 is derived for any online algorithm.

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Section: The Modelmentioning

confidence: 99%

Competitive FIB Aggregation for Independent Prefixes: Online Ski Rental on the Trie

Bieńkowski

Schmid

2013

Lecture Notes in Computer Science

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“…Some FIB compression work uses smart data structures to minimize storage size of FIB [21]. In [22], authors present a tunable aggregation algorithm with compressed prefix trees.…”

Section: Related Workmentioning

confidence: 99%

Toward Incremental FIB Aggregation with Quick Selections (FAQS)

Liu

Grigoryan

2018

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)

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Several approaches to mitigating the Forwarding Information Base (FIB) overflow problem were developed and software solutions using FIB aggregation are of particular interest. One of the greatest concerns to deploy these algorithms to real networks is their high running time and heavy computational overhead to handle thousands of FIB updates every second. In this work, we manage to use a single tree traversal to implement faster aggregation and update handling algorithm with much lower memory footprint than other existing work. We utilize 6year realistic IPv4 and IPv6 routing tables from 2011 to 2016 to evaluate the performance of our algorithm with various metrics. To the best of our knowledge, it is the first time that IPv6 FIB aggregation has been performed. Our new solution is 2.53 and 1.75 times as fast as the-state-of-the-art FIB aggregation algorithm for IPv4 and IPv6 FIBs, respectively, while achieving a near-optimal FIB aggregation ratio.

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“…Additionally, for the purposes of this paper our space bounds involve binary leaf-labeled tries only. We relax this restriction in [43] using the generic trie entropy measure of [17].…”

Section: Entropy Boundsmentioning

confidence: 99%

Compressing IP Forwarding Tables: Towards Entropy Bounds and Beyond

Rétvári

Tapolcai

Kő̈rösi

et al. 2016

IEEE/ACM Trans. Networking

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Lately, there has been an upsurge of interest in compressed data structures, aiming to pack ever larger quantities of information into constrained memory without sacrificing the efficiency of standard operations, like random access, search, or update. The main goal of this paper is to demonstrate how data compression can benefit the networking community by showing how to squeeze the IP Forwarding Information Base (FIB), the giant table consulted by IP routers to make forwarding decisions, into information-theoretical entropy bounds, with essentially zero cost on longest prefix match and FIB update. First, we adopt the state of the art in compressed data structures, yielding a static entropy-compressed FIB representation with asymptotically optimal lookup. Then, we redesign the venerable prefix tree, used commonly for IP lookup for at least 20 years in IP routers, to also admit entropy bounds and support lookup in optimal time and update in nearly optimal time. Evaluations on a Linux kernel prototype indicate that our compressors encode an FIB comprising more than 440 K prefixes to just about 100-400 kB of memory, with a threefold increase in lookup throughput and no penalty on FIB updates.Index Terms-Data compression, IP forwarding table lookup, prefix tree.

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Compressing IP forwarding tables for fun and profit

Cited by 6 publications

References 15 publications

Competitive FIB Aggregation for Independent Prefixes: Online Ski Rental on the Trie

Competitive FIB Aggregation for Independent Prefixes: Online Ski Rental on the Trie

Toward Incremental FIB Aggregation with Quick Selections (FAQS)

Compressing IP Forwarding Tables: Towards Entropy Bounds and Beyond

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