CLUE: Achieving Fast Update over Compressed Table for Parallel Lookup with Reduced Dynamic Redundancy

Yang, Tong; Duan, Ruian; Lü, Jian; Zhang, Shenjiang; Dai, Honghua; Liu, Bin

doi:10.1109/icdcs.2012.79

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…Generally, there are two kinds of IP lookup solutions. The first kind is hardware-based solutions, such as TCAM- 15 based solutions [3,4,5,6] and FPGA-based solutions [7,8,9,10]. For this kind of IP lookup solutions, compressing the FIBs can significantly save hardware cost and power consumption.…”

Section: Background and Motivationmentioning

confidence: 99%

Compressing IP Forwarding Tables with Small Bounded Update Time

Zhang

Wang

et al. 2016

Computer Networks

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Section: Background and Motivationmentioning

confidence: 99%

Compressing IP Forwarding Tables with Small Bounded Update Time

Zhang

Wang

et al. 2016

Computer Networks

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“…With the development of VLSI (Very Large Scale Integration) technologies, SRAM devices get faster and faster in speed but cheaper and cheaper in price than ever before, 7 Take the verifying program of Lulea as an example: for the same IP address, the program compares the next-hop of Lulea and binary trie. If and only if all the comparisons are equal by traversing IPv4's 32-bit space, the implementation of Lulea algorithm is correct; otherwise, the program stops and tells the prefix and the wrong next-hops.…”

Section: Related Workmentioning

confidence: 99%

“…In our previous work [7][8], we have carried out a series of studies and experiments on real routing tables and update messages, which are downloaded from www.ripe.net [9]. Via our data mining on routing update messages, we discover two significant characteristics of the routing updates: a) Stabilization Characteristic: Although the routing tables are huge, only a very minute fraction of prefixes are frequently updated.…”

Section: Introductionmentioning

confidence: 99%

An ultra-fast universal incremental update algorithm for trie-based routing lookup

Yang

Duan

et al. 2012

2012 20th IEEE International Conference on Network Protocols (ICNP)

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With the rapid growth of the Internet, the update messages in backbone routers become more and more frequent due to the ever-increasing dynamic changes on network topologies and new emerging functionalities of the Internet. In addition, update messages often come as a burst. Update action interrupts the packet lookup operation in the router's data plane, thus inefficient incremental update algorithm slows down IP lookup speed, and potentially badly degrades the system performance during bursty updates.Among trie-based routing lookup algorithms, binary trie 1 has the best update complexity O(W) (W is the maximum depth of the trie), but exhibits slow lookup speed, failing to be competent for forwarding tens of gigabit-per-second traffic in backbone routers. Therefore, various improved routing lookup algorithms are proposed to pursue high speed based on binary trie, but sacrificing the performance of incremental update. To minimize the interruption time that update operation incurs, we propose Blind Spot (BS) algorithm by picking out those updating nodes which would have produced domino effect 2 , achieving an update complexity of O(lookup+h) 3 , meanwhile keeping the lookup speed almost unchanged. Blind Spot algorithm is a universal methodology, which is applicable to all the trie-based lookup algorithms. To evaluate the performance of BS algorithm, we applied it to Lulea [1] and LC-trie [2] algorithms as two representatives. Extensive experimental resultsshow that both Lulea+BS and LC+BS algorithms achieve a much faster update speed than binary trie, while keeping the same lookup speed as the original Lulea and LC-trie algorithms.I.

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“…The fast increasing routing table incurs fast increasing FIB. For the routing lookup schemes based on software [8][9][10], FIB compression can be used to reduce their memory requirements; for the routing lookup algorithms based on TCAM [11][12][13], FIB compression can be used to reduce the hardware cost and power consumption. Therefore, a variety of FIB compression algorithms are proposed [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%