Accurate Per-Flow Measurement with Bloom Sketch

被引：0

作者：

Zhou, Yang ^{[1
]}

Jin, Hao ^{[1
]}

Liu, Peng ^{[1
]}

Zhang, Haowei ^{[1
]}

Yang, Tong ^{[1
]}

Li, Xiaoming ^{[1
]}

机构：

[1] Peking Univ, Beijing, Peoples R China

来源：

IEEE INFOCOM 2018 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS) | 2018年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Sketch is a probabilistic data structure, and is widely used for per-flow measurement in network. The most common sketches are the CM sketch and its several variants. However, given a limited memory size, these sketches always significantly overestimate some flows, exhibiting poor accuracy. To address this issue, we proposed a novel sketch named the Bloom sketch, combining the sketch with the Bloom filter, another well-known probabilistic data structure widely used for membership queries. Extensive experiments based on real IP traces show that our Bloom sketch achieves up to 14:47x higher accurac y compared with the CM sketch, while exhibiting comparable insertion and query speed. Our source code is available at Github [1].

引用

页数：2

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