1 / 17

ABC : Adaptive Binary Cuttings for Multidimensional Packet Classification

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

nadine-snyder
Download Presentation

ABC : Adaptive Binary Cuttings for Multidimensional Packet Classification

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Outline • Observations • Algorithm Description • Algorithm Optimizations • Performance Evaluation

  3. 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.

  4. Algorithm Description • DT : Decision Tree • CST : Cutting Shape Tree • CSB : Encode each CST with a Cutting Shape Bitmap.

  5. 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.

  6. Algorithm Description • preference value :

  7. 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.

  8. Algorithm Description i : current index in CSB j: the current index in CDV. Next index i’ in CSB is Next index j’ in CDV is

  9. Algorithm Description • ABC Variation II • Generate up to D separate CSTs, each for one dimension.

  10. 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

  11. Algorithm Description

  12. Algorithm Description

  13. 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.

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

  15. Performance Evaluation

  16. Performance Evaluation

  17. Performance Evaluation

More Related