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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