1. Zhaogeng Li, Jun Bi, Sen Wang, and Xiaoke Jiang Asia FI Workshop in Kyoto, 2012 Sho Harada Park Lab Nov 29 th, 2012

2. 1. Introduction 2. CCN (Content Centric Networking) 3. Bloom Filter 4. Architecture 5. Problem 6. United Bloom Filter 7. Error Handling 8. Experiments 9. Conclusion 10. Reference 2

3.  CCN was developed to solve many network problems that is being occurred from increasing traffic.  It is one of the most promising architectures as a Future Internet architecture.  CCN router uses three tables that store data.  This proposal enables us to compress the size of the table. 3

4.  Packet  Interest Packet : Used to request a content.  Data Packet : Used to send the content.  CCN router  CS (Content Store) : Cache contents.  PIT (Pending Interest Table) : Record name and face to define where to forward Data Packet.  FIB (Forwarding Information Base) : Record face to decide where to forward Interest Packet. 4

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11.  Bloom Filter is introduced in PIT.  Content Name is converted by hash function and added to Bloom Filter of the appropriate face. 11

12. Bloom FilterFace 000000000 1 2 NameFace Youtube/Video.mp41 01 2 PIT FIB 12

13. Bloom FilterFace 000000000 1 2 NameFace Youtube/Video.mp41 01 2 PIT FIB Interest “Youtube/Video.mp4” 13

14. Bloom FilterFace 010101010 000000001 2 NameFace Youtube/Video.mp41 01 2 PIT FIB Interest “Youtube/Video.mp4” H( “Youtube/Video.mp4” ) = “01010101” 14

15. Bloom FilterFace 010101010 000000001 2 NameFace Youtube/Video.mp41 01 2 PIT FIB Data “Youtube/Video.mp4” H( “Youtube/Video.mp4” ) = “01010101” 15

16. Bloom FilterFace 010111110 000000001 010101112 NameFace Youtube/Video.mp41 01 2 PIT FIB Data “Youtube/Video.mp4” H( “Youtube/Video.mp4” ) = “01010101” H( “Youtube/Video2.mp4” ) = “00001111” 16

17. Bloom FilterFace 000010100 000000001 000000102 NameFace Youtube/Video.mp41 01 2 PIT FIB Data “Youtube/Video.mp4” H( “Youtube/Video.mp4” ) = “01010101” H( “Youtube/Video2.mp4” ) = “00001111” Data “Youtube/Video.mp4” 17

18. Bloom FilterFace 011101010 000000001 2 NameFace Youtube/Video.mp41 01 2 PIT FIB Interest “Youtube/Video.mp4” H( “Youtube/Video.mp4” ) = “01010101” 18

19.  Use two Bloom Filters in one face.  Filter shifts active and inactive.  When a Bloom Filter stops, it will be initialized. 19

20. Time Filter 1 Filter 2 Filter 1 = “01010101” (Active) Filter 2 = “00000000” 20

21. Time Filter 1 Filter 2 Filter 1 = “01010101” (Active) Filter 2 = “00000000” (Record) 21

22. Time Filter 1 Filter 2 Filter 1 = “00000000” Filter 2 = “00111100” (Active) 22

23.  The result of experiment shows that the probability of false positive was less than 0.1 %.  If an Interest Packet was dropped, the requester sends Interest Packet again.  Data may be forwarded by false positive. But the Data Packet will be dropped by the next node. 23

24. 24 BF : 1MB Interest Data Interest Data

25.  Compression of PIT : 40% reduced  Probability of False Positive : 0.027% 25

26.  Introducing Bloom Filter, the compression of PIT is realized.  When we use Bloom Filter, we need to think of False Positive. ⇒ Experiment shows the probability of False Positive was only 0.027 %. Therefore, it will not make a big problem. We have only to deal with False Positive when it happens. 26

27.  Zhaogeng Li, Jun Bi, Sen Wang, and Xiaoke Jiang, “The Compression of PIT with Bloom Filter in CCN”, Asia FI Workshop in Kyoto, 2012. 27

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