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Enhancing FIB Lookup Efficiency in Content-Centric Networking via Parallelization

This paper presents advancements in the Forwarding Information Base (FIB) lookup process within Content-Centric Networking (CCN). Utilizing the TILEPro64 multicore platform, the authors, Shuai Ding, Zhen Chen, and Zhi Liu, propose and evaluate two parallelized lookup algorithms based on hash tables and Bloom filters. By generating a FIB and interest models from real-world data, the study demonstrates how leveraging a multi-core architecture significantly accelerates lookup performance. Experiment results indicate improved efficiency, supporting the transition towards more effective network routers.

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Enhancing FIB Lookup Efficiency in Content-Centric Networking via Parallelization

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  1. Parallelizing FIB Lookup in Content Centric Networking Authors: ShuaiDing, Zhen Chen, and Zhi Liu Publisher:ICNDC 2012 Presenter: Chai-Yi Chu Date: 2013/03/20

  2. Outline • Introduction • Implementation • Generating FIB and Interest • Parallelizing FIB Lookup • Experiment

  3. Introduction • Leverages multi-core platform to accelerate the FIB lookup in CCN router. • Based on TILEPro64 platform, which has 64 identical tiles. • Two parallelized lookup algorithms • based on hash table. • based on Bloom filter

  4. Use a special server called ASN server, which returns corresponding AS number upon receiving a query of domain name.

  5. Implementation • TILEPro64 multicore platform • 64 identical tiles, each of which is a full featured processor. • 43 of them are available to user space programs. • FIB Generator • generates FIB entries. • Interest Generator • generates Interests to search in FIB. • FIB • implements lookup algorithms.

  6. Generating FIB and Interest • Simulate CCN Interests with http URL requests. • extract 50,000 URLs from realistic pcap files captured at the gateway of an office. • Generate FIB • we set a seed for a random number generator and use statistics of domain names. • Generate Interest • generate a FIB prefix using the same seed as generating FIB. • produce the suffix using another random number generator based on the statistics of URL path.

  7. Parallelizing FIB Lookup • Hash table based lookup algorithm • Start by the longest prefix, each prefix is searched in FIB until a certain prefix matching is found.

  8. Bloom filter is searched at first, if a prefix doesn’t exist in Bloom filter, there is no need to search for it in the hash table any more.

  9. Experiment • load factor is 1.0

  10. load factor is 10.0

  11. Hash based

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