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Abstract
International audience; Identifying heavy-hitter traffic flows efficiently and accurately is essential for Internet security, accounting and traffic engineering. However, finding all heavy-hitters might require large memory for storage of flows information that is incompatible with the usage of fast and small memory. Moreover, upcoming 100Gbps transmission rates make this recognition more challenging. How to improve the accuracy of heavy-hitters identification with limited memory space has become a critical issue. This paper presents a scalable algorithm named Mnemonic Lossy Counting (MLC) that improves the accuracy of heavy-hitters identification while having a reasonable time and space complexity. MLC algorithm holds potential candidate heavy-hitters in a historical information table. This table is used to obtain tighter error bounds on the estimated sizes of candidate heavy-hitters. We validate the MLC algorithm using real network traffic traces, and we compared its performance with two state-of-theart algorithms, namely Lossy Counting (LC) and Probabilistic Lossy Counting (PLC). The results reveal that: 1) with same set of parameters and memory usage, MLC achieves between 31.5% and 6.67% fewer false positives than LC and PLC. 2) MLC and LC have a zero false negative ratio, whereas 38% of the cases PLC has a non-zero false negatives and PLC can miss up to 4.4% of heavy-hitters. 3) MLC has a slightly lower memory cost than LC during the first few windows and its memory usage decreases with time, when PLC memory usage declines sharply. 4) MLC has similar runtime than LC, and smaller time than PLC.
Original document
The different versions of the original document can be found in:
- https://hal.archives-ouvertes.fr/hal-00527094/document,
- https://hal.archives-ouvertes.fr/hal-00527094,
- https://dblp.uni-trier.de/db/conf/ipccc/ipccc2010.html#RongZXS10,
- https://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5682303,
- http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5682303,
- https://academic.microsoft.com/#/detail/2102231621
Document information
Published on 01/01/2010
Volume 2010, 2010
DOI: 10.1109/pccc.2010.5682303
Licence: CC BY-NC-SA license
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