ABC : Adaptive Binary Cuttings for Multidimensional Packet Classification

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ABC : Adaptive Binary Cuttings for Multidimensional Packet Classification. Publisher : TRANSACTIONS ON NETWORKING Author : Haoyu Song, Jonathan S. Turner Presenter : Yu-Hsiang Wang Date : 2012/05/09. Outline. Observations Algorithm Description Algorithm Optimizations - PowerPoint PPT Presentation

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ABC : Adaptive Binary Cuttings for Multidimensional Packet Classification

Publisher : TRANSACTIONS ON NETWORKINGAuthor : Haoyu Song, Jonathan S. TurnerPresenter : Yu-Hsiang WangDate : 2012/05/09

1

Outline

ObservationsAlgorithm DescriptionAlgorithm OptimizationsPerformance Evaluation

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Observations

• In HiCuts and HyperCuts, a global expansion factor may not be suitable for all nodes. Bucket Size cannot guarantee either throughput or storage.

• Our goal is to make the “optimal” decisions that consistently improve the throughput until the given storage is used up.

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Algorithm Description

• DT : Decision Tree• CST : Cutting Shape Tree• CSB : Encode each CST with a Cutting Shape

Bitmap.

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Algorithm Description

•ABC Variation I ▫The maximum number of cuttings is

constrained by the DT node size.

▫Choose one of the subregions produced so far and split it into two equal-sized subregions along a certain dimension until we run out of space in the DT node.

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Algorithm Description

• preference value :

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Algorithm Description

• If the current number of leaf nodes is less than k, we choose one leaf node to cut on a specific dimension.

• Our goal is to find the leaf node i and the dimension d that can minimize the preference value.

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Algorithm Description

i : current index in CSBj: the current indexin CDV.

Next index i’ in CSB is

Next index j’ in CDV is

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Algorithm Description

•ABC Variation II ▫Generate up to D separate CSTs, each for

one dimension.

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Algorithm Description

•ABC Variation III▫Any bit can be chosen to split the filter set

•Assume DT size = 128 bits ▫ABC Variation I = 22 cuts▫ABC Variation III = 13 cuts

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Algorithm Description

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Algorithm Description

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Algorithm Optimizations

•Reduce Filters Using a Hash Table.•Filter Partition on the Protocol Field.•Partitioning Filters Based on Duplication

Factor.•Holding Filters Internally and Reversing

Search Order.

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Performance Evaluation

• Performance : bytes retrieved per lookup• Scalability on Filter Set Size

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Performance Evaluation

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Performance Evaluation

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Performance Evaluation

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