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New directions in traffic measurement and accounting

New directions in traffic measurement and accounting New Directions in Traf c Measurement and Accounting Cristian Estan Computer Science and Engineering Department University of California, San Diego 9500 Gilman Drive La Jolla, CA 92093-0114 George Varghese Computer Science and Engineering Department University of California, San Diego 9500 Gilman Drive La Jolla, CA 92093-0114 cestan@cs.ucsd.edu ABSTRACT Accurate network tra ƒc measurement is required for accounting, bandwidth provisioning and detecting DoS attacks. These applications see the tra ƒc as a collection of ‚ows they need to measure. As link speeds and the number of ‚ows increase, keeping a counter for each ‚ow is too expensive (using SRAM) or slow (using DRAM). The current state-of-the-art methods (Cisco ™s sampled NetFlow) which log periodically sampled packets are slow, inaccurate and resource-intensive. Previous work showed that at di €erent granularities a small number of œheavy hitters  accounts for a large share of tra ƒc. Our paper introduces a paradigm shift for measurement by concentrating only on large ‚ows ” those above some threshold such as 0.1% of the link capacity. We propose two novel and scalable algorithms for identifying the large ‚ows: sample and hold and multistage lters, which take a constant number of memory references per packet and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

New directions in traffic measurement and accounting

Association for Computing Machinery — Aug 19, 2002

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Datasource
Association for Computing Machinery
Copyright
Copyright © 2002 by ACM Inc.
ISBN
1-58113-570-X
doi
10.1145/633025.633056
Publisher site
See Article on Publisher Site

Abstract

New Directions in Traf c Measurement and Accounting Cristian Estan Computer Science and Engineering Department University of California, San Diego 9500 Gilman Drive La Jolla, CA 92093-0114 George Varghese Computer Science and Engineering Department University of California, San Diego 9500 Gilman Drive La Jolla, CA 92093-0114 cestan@cs.ucsd.edu ABSTRACT Accurate network tra ƒc measurement is required for accounting, bandwidth provisioning and detecting DoS attacks. These applications see the tra ƒc as a collection of ‚ows they need to measure. As link speeds and the number of ‚ows increase, keeping a counter for each ‚ow is too expensive (using SRAM) or slow (using DRAM). The current state-of-the-art methods (Cisco ™s sampled NetFlow) which log periodically sampled packets are slow, inaccurate and resource-intensive. Previous work showed that at di €erent granularities a small number of œheavy hitters  accounts for a large share of tra ƒc. Our paper introduces a paradigm shift for measurement by concentrating only on large ‚ows ” those above some threshold such as 0.1% of the link capacity. We propose two novel and scalable algorithms for identifying the large ‚ows: sample and hold and multistage lters, which take a constant number of memory references per packet and

There are no references for this article.