Simple and Accurate Identification of High-Rate Flows by Packet Sampling

Unfairness among best-effort flows is a serious problem on the Internet. In particular, UDP flows or unresponsive flows that do not obey the TCP flow control mechanism can consume a large share of the available bandwidth. High-rate flows seriously affect other flows, so it is important to identify them and limit their throughput by selectively dropping their packets. As link transmission capacity increases and the number of active flows increases, however, capturing all packet information becomes more difficult. In this paper, we propose a novel method of identifying high-rate flows by using sampled packets. The proposed method simply identifies flows from which Y packets are sampled without timeout. The identification principle is very simple and the implementation is easy. We derive the identification probability for flows with arbitrary flow rates and obtain an identification curve that clearly demonstrates the accuracy of identification. The characteristics of this method are determined by three parameters: the identification threshold Y , the timeout coefficient K, and the sampling interval N . To match the experimental identification probability to the theoretical one and to simplify the identification mechanism, we should set K to the maximum allowable value. Although increasing Y improves the identification accuracy, both the required memory size and the processing power grow as Y increases. Numerical evaluation using an actual packet trace demonstrated that the proposed method achieves very high identification accuracy with a much simpler mechanism than that of previously proposed methods.

[1]  Konstantinos Psounis,et al.  CHOKe - a stateless active queue management scheme for approximating fair bandwidth allocation , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[2]  Yin Zhang,et al.  On the characteristics and origins of internet flow rates , 2002, SIGCOMM '02.

[3]  Sally Floyd,et al.  Promoting the use of end-to-end congestion control in the Internet , 1999, TNET.

[4]  Kang G. Shin,et al.  The BLUE active queue management algorithms , 2002, TNET.

[5]  Fang Hao,et al.  Fast, memory-efficient traffic estimation by coincidence counting , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[6]  Murali S. Kodialam,et al.  Runs based traffic estimator (RATE): a simple, memory efficient scheme for per-flow rate estimation , 2004, IEEE INFOCOM 2004.

[7]  Ratul Mahajan,et al.  Controlling high-bandwidth flows at the congested router , 2001, Proceedings Ninth International Conference on Network Protocols. ICNP 2001.

[8]  T. V. Lakshman,et al.  SRED: stabilized RED , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[9]  A. Kumar,et al.  Space-code bloom filter for efficient per-flow traffic measurement , 2004, IEEE INFOCOM 2004.

[10]  George Varghese,et al.  New directions in traffic measurement and accounting , 2002, CCRV.

[11]  George Varghese,et al.  Building a better NetFlow , 2004, SIGCOMM.

[12]  Nicolas Hohn,et al.  Inverting sampled traffic , 2003, IEEE/ACM Transactions on Networking.

[13]  Shigeki Goto,et al.  Identifying elephant flows through periodically sampled packets , 2004, IMC '04.

[14]  A. L. Narasimha Reddy,et al.  Identifying Long-Term High-Bandwidth Flows at a Router , 2001, HiPC.

[15]  Carsten Lund,et al.  Properties and prediction of flow statistics from sampled packet streams , 2002, IMW '02.