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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
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THE COMPRESSION OF PIT WITH BLOOM FILTER IN CCN
Zhaogeng Li, Jun Bi, Sen Wang, and Xiaoke JiangAsia FI Workshop in Kyoto, 2012
Sho HaradaPark Lab
Nov 29th, 2012
OUTLINE1. Introduction2. CCN (Content Centric Networking)3. Bloom Filter4. Architecture5. Problem6. United Bloom Filter7. Error Handling8. Experiments9. Conclusion10. Reference
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1. INTRODUCTION 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.
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2. CCN (CONTENT CENTRIC NETWORKING)
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.
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2. CCN (CONT.)
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3. BLOOM FILTER
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3. BLOOM FILTER (CONT.)
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3. BLOOM FILTER (CONT.)
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3. BLOOM FILTER (CONT.)
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3. BLOOM FILTER (CONT.)
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4. ARCHITECTURE
Bloom Filter is introduced in PIT.
Content Name is converted by hash function and added to Bloom Filter of the appropriate face.
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4. ARCHITECTURE (CONT.)
Bloom Filter
Face
00000000 000000000 100000000 2Name Fac
eYoutube/Video.mp4
1
0 1
2
PIT
FIB
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4. ARCHITECTURE (CONT.)
Bloom Filter
Face
00000000 000000000 100000000 2Name Fac
eYoutube/Video.mp4
1
0 1
2
PIT
FIB
Interest“Youtube/Video.mp4”
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4. ARCHITECTURE (CONT.)
Bloom Filter
Face
01010101 000000000 100000000 2Name Fac
eYoutube/Video.mp4
1
0 1
2
PIT
FIB
Interest“Youtube/Video.mp4”
H( “Youtube/Video.mp4” ) = “01010101”
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4. ARCHITECTURE (CONT.)
Bloom Filter
Face
01010101 000000000 100000000 2Name Fac
eYoutube/Video.mp4
1
0 1
2
PIT
FIB
Data“Youtube/Video.mp4”
H( “Youtube/Video.mp4” ) = “01010101”
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5. PROBLEM
Bloom Filter
Face
01011111 000000000 101010111 2Name Fac
eYoutube/Video.mp4
1
0 1
2
PIT
FIB
Data“Youtube/Video.mp4”
H( “Youtube/Video.mp4” ) = “01010101”H( “Youtube/Video2.mp4” ) = “00001111”
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5. PROBLEM (CONT.)
Bloom Filter
Face
00001010 000000000 100000010 2Name Fac
eYoutube/Video.mp4
1
0 1
2
PIT
FIB
Data“Youtube/Video.mp4”
H( “Youtube/Video.mp4” ) = “01010101”H( “Youtube/Video2.mp4” ) = “00001111”
Data“Youtube/Video.mp4”
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5. PROBLEM (CONT.)
Bloom Filter
Face
01110101 000000000 100000000 2Name Fac
eYoutube/Video.mp4
1
0 1
2
PIT
FIB
Interest“Youtube/Video.mp4”
H( “Youtube/Video.mp4” ) = “01010101”
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6. UNITED BLOOM FILTER
Use two Bloom Filters in one face.
Filter shifts active and inactive.
When a Bloom Filter stops, it will be initialized.
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6. UNITED BLOOM FILTER (CONT.)
Time
Filter 1Filter 2
Filter 1 = “01010101” (Active)
Filter 2 = “00000000”
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6. UNITED BLOOM FILTER (CONT.)
Time
Filter 1Filter 2
Filter 1 = “01010101” (Active)
Filter 2 = “00000000” (Record)
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6. UNITED BLOOM FILTER (CONT.)
Time
Filter 1Filter 2
Filter 1 = “00000000”
Filter 2 = “00111100” (Active)
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7. ERROR HANDLING
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.
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8. EXPERIMENTS
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BF : 1MB
Intere
st
Intere
st
Data
Interest
Interest Data
Data
Data
8. EXPERIMENTS (CONT.)
Compression of PIT : 40% reduced
Probability of False Positive : 0.027%
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9. CONCLUSION 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.
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10. REFERENCE
Zhaogeng Li, Jun Bi, Sen Wang, and Xiaoke Jiang, “The Compression of PIT with Bloom Filter in CCN”, Asia FI Workshop in Kyoto, 2012.
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