THE COMPRESSION OF PIT
WITH BLOOM FILTER IN CCN
Zhaogeng Li, Jun Bi, Sen Wang, and Xiaoke Jiang
Asia FI Workshop in Kyoto, 2012
Sho Harada
Park Lab
Nov 29th, 2012
OUTLINE
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Introduction
CCN (Content Centric Networking)
Bloom Filter
Architecture
Problem
United Bloom Filter
Error Handling
Experiments
Conclusion
Reference
2
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.

3
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.
4
2. CCN (CONT.)
5
3. BLOOM FILTER
6
3. BLOOM FILTER (CONT.)
7
3. BLOOM FILTER (CONT.)
8
3. BLOOM FILTER (CONT.)
9
3. BLOOM FILTER (CONT.)
10
4. ARCHITECTURE

Bloom Filter is introduced in PIT.

Content Name is converted by hash function
and added to Bloom Filter of the appropriate
face.
11
4. ARCHITECTURE (CONT.)
0
1
2
PIT
Bloom Filter
Face
00000000
0
00000000
1
00000000
FIB
2
Name
Face
Youtube/Video.mp4
1
12
4. ARCHITECTURE (CONT.)
0
1
Interest
“Youtube/Video.mp4”
2
PIT
Bloom Filter
Face
00000000
0
00000000
1
00000000
FIB
2
Name
Face
Youtube/Video.mp4
1
13
4. ARCHITECTURE (CONT.)
Interest
“Youtube/Video.mp4”
0
1
2
PIT
H( “Youtube/Video.mp4” )
= “01010101”
Bloom Filter
Face
01010101
0
00000000
1
00000000
FIB
2
Name
Face
Youtube/Video.mp4
1
14
4. ARCHITECTURE (CONT.)
Data
“Youtube/Video.mp4”
0
1
2
PIT
H( “Youtube/Video.mp4” )
= “01010101”
Bloom Filter
Face
01010101
0
00000000
1
00000000
FIB
2
Name
Face
Youtube/Video.mp4
1
15
5. PROBLEM
Data
“Youtube/Video.mp4”
0
1
2
H( “Youtube/Video.mp4” )
= “01010101”
H( “Youtube/Video2.mp4” )
= “00001111”
PIT
Bloom Filter
Face
01011111
0
00000000
1
01010111
FIB
2
Name
Face
Youtube/Video.mp4
1
16
5. PROBLEM (CONT.)
Data
“Youtube/Video.mp4”
0
1
2
H( “Youtube/Video.mp4” )
= “01010101”
H( “Youtube/Video2.mp4” )
= “00001111”
PIT
Bloom Filter
Face
00001010
0
00000000
1
00000010
FIB
2
Name
Face
Youtube/Video.mp4
1
17
5. PROBLEM (CONT.)
0
1
Interest
“Youtube/Video.mp4”
2
PIT
H( “Youtube/Video.mp4” )
= “01010101”
Bloom Filter
Face
01110101
0
00000000
1
00000000
FIB
2
Name
Face
Youtube/Video.mp4
1
18
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.
19
6. UNITED BLOOM FILTER (CONT.)
Time
Filter 1
Filter 2
Filter 1 = “01010101” (Active)
Filter 2 = “00000000”
20
6. UNITED BLOOM FILTER (CONT.)
Time
Filter 1
Filter 2
Filter 1 = “01010101” (Active)
Filter 2 = “00000000” (Record)
21
6. UNITED BLOOM FILTER (CONT.)
Time
Filter 1
Filter 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
BF : 1MB
24
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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