Parallelizing FIB Lookup in Content Centric Networking

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

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

8. 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.

10. 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.

11. 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.

13. 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.

15. Experiment • load factor is 1.0

16. load factor is 10.0

17. Hash based