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New directions in traffic measurement and accounting
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Source Applications, Technologies, Architectures, and Protocols for Computer Communication archive
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications table of contents
Pittsburgh, Pennsylvania, USA
SESSION: Measuring paths and flows table of contents
Pages: 323 - 336  
Year of Publication: 2002
ISBN:1-58113-570-X
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Authors
Cristian Estan  University of California, San Diego, La Jolla, CA
George Varghese  University of California, San Diego, La Jolla, CA
Sponsors
ACM: Association for Computing Machinery
SIGCOMM: ACM Special Interest Group on Data Communication
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 18,   Downloads (12 Months): 100,   Citation Count: 65
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ABSTRACT

Accurate network traffic measurement is required for accounting, bandwidth provisioning and detecting DoS attacks. These applications see the traffic as a collection of flows they need to measure. As link speeds and the number of flows increase, keeping a counter for each flow 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 different granularities a small number of "heavy hitters" accounts for a large share of traffic. Our paper introduces a paradigm shift for measurement by concentrating only on large flows --- those above some threshold such as 0.1% of the link capacity.We propose two novel and scalable algorithms for identifying the large flows: sample and hold and multistage filters, which take a constant number of memory references per packet and use a small amount of memory. If $M$ is the available memory, we show analytically that the errors of our new algorithms are proportional to $1/M$; by contrast, the error of an algorithm based on classical sampling is proportional to $1/\sqrtM$, thus providing much less accuracy for the same amount of memory. We also describe further optimizations such as early removal and conservative update that further improve the accuracy of our algorithms, as measured on real traffic traces, by an order of magnitude. Our schemes allow a new form of accounting called threshold accounting in which only flows above a threshold are charged by usage while the rest are charged a fixed fee. Threshold accounting generalizes usage-based and duration based pricing.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  65
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Collaborative Colleagues:
Cristian Estan: colleagues
George Varghese: colleagues

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