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	An adaptive method for identifying heavy hitters combining sampling and data streaming counting
	Li, Zhen ; Yang, Yahui ; Zhang, Guangxing ; Qin, Guangcheng
	2010
英文摘要	Identifying heavy hitters is essential for network monitoring, management, charging and etc. Existing methods in the literature have some limitations. How to reduce the memory consumption effectively without compromising identification accuracy is still challenging. In this paper, an adaptive method combining sampling and data streaming counting is proposed, called FSPLC(feedback sampling probabilistic lossy counting). Based on the history information in the flow counter table, FSPLC can adjust the sampling frequency dynamically, and also adapt to the real-time traffic changes. Comparison with state-of-the-art algorithms based on real Internet traces suggests that FSPLC is remarkably efficient and accurate. Experiment results show that FSPLC has 1) 60% lower memory consumption, 2) 15% smaller false-positive ratio. ? 2010 IEEE.; EI; 0
语种	英语
DOI标识	10.1109/ICACTE.2010.5579256
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/329819]
专题	软件与微电子学院
推荐引用方式 GB/T 7714	Li, Zhen,Yang, Yahui,Zhang, Guangxing,et al. An adaptive method for identifying heavy hitters combining sampling and data streaming counting. 2010-01-01.

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