A Difference Resolution Approach to Compressing Access Control Lists

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A Difference Resolution Approach to Compressing Access Control Lists. James Daly, Alex Liu, Eric Torng Michigan State University INFOCOM 2013. Motivation. Classifiers used for many applications Packet Forwarding Firewalls Quality of Service Classifiers are growing New threats - PowerPoint PPT Presentation

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A Difference Resolution Approach to Compressing Access Control ListsJames Daly,Alex Liu, Eric TorngMichigan State UniversityINFOCOM 2013

Motivation• Classifiers used for many applications• Packet Forwarding• Firewalls• Quality of Service

• Classifiers are growing• New threats• New services

2

Motivation• Classifier compression is an important problem• Device imposed rule limits

• NetScreen-100 allows only 733 rules• Simplifies rule management

• DIFANE [Yu et al. SIGCOMM 2010]

3

BackgroundF1 F2 Color

1 3 White

3 3 White

1-3 1 White

1-3 5 White

1-3 1-5 Black

4

F1 F2 Color

2 3 Black

1-3 3 White

1-3 2-4 Black

1-3 1-5 White

Packet: [2, 4]

Classifier Definition• Classifier : list of rules• Tuple of d intervals over finite, discrete fields• Decision (accept, deny, physical port number, etc.)

• Only first matching rule applies• Classifiers equivalent if they give the same result for all inputs

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F1 F2 Color

1 3 White

3 3 White

1-3 1 White

1-3 5 White

1-3 1-5 Black

F1 F2 Color

2 3 Black

1-3 3 White

1-3 2-4 Black

1-3 1-5 White

Problem Definition• Problem• Input: classifier• Output: smallest equivalent classifier• NP-Hard

66

F1 F2 Color

1 3 White

3 3 White

1-3 1 White

1-3 5 White

1-3 1-5 Black

F1 F2 Color

2 3 Black

1-3 3 White

1-3 2-4 Black

1-3 1-5 White

Prior Work• Redundancy Removal [eg. Liu and Gouda. DBSec 2005]• Iterated Strip Rule [Applegate et al. SODA 2007]• Only two dimensions• Approximation guarantee: O(min(n1/3, Opt1/2))

• Firewall Compressor [Liu et al. INFOCOM 2008]• Optimal weighted 1-D case• Works on higher dimensions

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Motivating Example

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Dimension Reduction

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FC: Fully Solve Each Row

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X Y Color

2 2-3 Green

2 5-6 Red

2 4-8 White

2 1-9 Black

4 5 Red

4 6-7 Blue

4 3-8 White

4 1-9 Black

1-4 5-6 Red

1-4 3-8 White

1-4 1-9 Black

X Y Color

2 2-3 Green

2 5-6 Red

2 4-8 White

2 1-9 Black

4 5 Red

4 6-7 Blue

4 3-8 White

4 1-9 Black

X Y Color

2 2-3 Green

2 5-6 Red

2 4-8 White

2 1-9 Black

Diplomat: Identify and Resolve Differences

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X Y Color

2-3 2 Green

Diplomat: Identify and Resolve Differences

12

X Y Color

2-3 2 Green

Diplomat: Identify and Resolve Differences

13

X Y Color

2-3 2 Green

X Y Color

2-3 2 Green

6-7 4 Blue

Diplomat: Identify and Resolve Differences

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X Y Color

2-3 2 Green

6-7 4 Blue

X Y Color

2-3 2 Green

6-7 4 Blue

5-6 1-4 Red

3-8 1-4 White

1-9 1-4 Black

Higher Dimensions

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Diplomat• Three parts• Base solver for the last row

• Firewall Compressor for 1D case• Diplomat otherwise

• Resolver• Given two rows identify and resolve differences• Merge rows together into one

• Scheduler• Find best order to resolve rows

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F1 F2 Color

1 1-5 White

2 5-9 White

F1 F2 Color

1-1 1-5 White

1 6 Black

1 8 Black

Different Resolvers

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F1 F2 Color

1 1-5 White

2 5-9 White

1-2 2 Black

1-2 4 Black

1-2 6 Black

1-2 8 Black

1-2 1-9 White

F1 F2 Color

1 1-5 White

1 6 Black

1 8 Black

1-2 2 Black

1-2 4 Black

1-2 1-9 White

Scheduling

18• Multi-row resolver: greedy schedule• Single-row resolver: dynamic programming schedule

Dynamic Schedule1 2 3 4

1 0 2 0 2

2 1 0 1 3

3 0 2 0 2

4 1 3 1 0

1 2 3 4

1 1:0 1:12:2

1:12:43:1

1:22:33:24:3

2 2:0 2:23:1

2:33:24:3

3 3:0 3:14:2

4 4:0

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Remaining Row

Sour

ce R

ow

Upper Bound

Low

er B

ound

Results

• Comparison of Firewall Compressor and Diplomat on 40 real-life classifiers• Divided into sets based on

size• Diplomat requires 30%

fewer rules on largest sets• 2-D bounds: O(min(n1/3, Opt1/2))

Set Firewall Compressor

Diplomat

Small 67.4% 67.2%

Medium 50.8% 45.7%

Large 44.5% 30.2%

All 56.1% 50.6%

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Mean Compression Ratio

Conclusion• Diplomat offers significant improvements over Firewall

Compressor because it focuses on the differences between rows

• Results are most pronounced on larger classifiers• Can guarantee approximation bound for 2-D classifiers

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Questions?

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