.png){kind=icon}
返回
ABC: Adaptive Binary Cuttings for Multidimensional Packet Classification
.png){kind=icon}
OA
AI
DOI：10.1109/TNET.2012.2190519.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Decision tree-based packet classification algorithms are easy to implement and allow the tradeoff between storage and throughput. However, the memory consumption of these algorithms remains quite high when high throughput is required. The Adaptive Binary Cuttings (ABC) algorithm exploits another degree of freedom to make the decision tree adapt to the geometric distribution of the filters. The three variations of the adaptive cutting procedure produce a set of different-sized cuts at each decision step, with the goal to balance the distribution of filters and to reduce the filter duplication effect. The ABC algorithm uses stronger and more straightforward criteria for decision tree construction. Coupled with an efficient node encoding scheme, it enables a smaller, shorter, and well-balanced decision tree. The hardware-oriented implementation of each variation is proposed and evaluated extensively to demonstrate its scalability and sensitivity to different configurations. The results show that the ABC algorithm significantly outperforms the other decision tree-based algorithms. It can sustain more than 10-Gb/s throughput and is the only algorithm among the existing well-known packet classification algorithms that can compete with TCAMs in terms of the storage efficiency.
Keyword:
Decision tree
packet classification
期刊
I
IF:
3.6
论文数:
4.4K
被引数:
9.5K
机构
引用论文
.png){kind=icon}
Exposure to seawater increases intestinal motility in euryhaline rainbow trout (Oncorhynchus mykiss)
OAAI
OAAI分享收藏分享收藏分享收藏Hetarenium salts from pentafluoropyridine. Syntheses, spectroscopic properties, and applications
PREAI
分享收藏
查看更多内容