abc : adaptive binary cuttings for multidimensional packet classification
DESCRIPTION
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 PresentationTRANSCRIPT
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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