NDLI: An adaptive method for identifying heavy hitters combining sampling and data streaming counting

Content Provider	IEEE Xplore Digital Library
Author	Zhen Li Yahui Yang Guangxing Zhang Guangcheng Qin
Copyright Year	2010
Description	Author affiliation: Communication Center, the 61st Research Institute of General Staff, China (Guangcheng Qin) \|\| School of Software and Microelectronions, Peking University, China (Zhen Li; Yahui Yang) \|\| Institute of Computing Technology, Chinese Academy Sciences, China (Guangxing Zhang)
Abstract	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.
File Size	688266
File Format	PDF
ISBN	9781424465392
ISSN	21547491
e-ISBN	9781424465422
DOI	10.1109/ICACTE.2010.5579256
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	adaptive heavy hitters Memory management Heat engines data streaming counting sampling Indexes Monitoring
Content Type	Text
Resource Type	Article

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