Fluid models and solutions for large-scale IP networks

ABSTRACT

In this paper we present a scalable model of a network of Active Queue Management (AQM) routers serving a large population of TCP flows. We present efficient solution techniques that allow one to obtain the transient behavior of the average queue lengths, packet loss probabilities, and average end-to-end latencies. We model different versions of TCP as well as different versions of RED, the most popular AQM scheme currently in use. Comparisons between our models and <tt>ns</tt> simulation show our models to be quite accurate while at the same time requiring substantially less time to solve, especially when workloads and bandwidths are high.

REFERENCES

• 1: DARPA NMS Baseline Network Topology. http://www.cs.dartmouth.edu/ nicol/NMS/baseline/.
• 2: Parallel and Distributed NS. http://www.cc.gatech.edu/computing/compass/pdns/.
• 3: Scalable Simulation Framework (SSFNet). http://www.ssfnet.org.
• 4: The Network Simulator - ns-2. http://www.isi.edu/nsnam/ns/.
• 5: Virtual InterNetwork Testbed. http://www.isi.edu/nsnam/vint/.
• 6: Eitan Altman , Konstantin Avrachenkov , Chadi Barakat, A stochastic model of TCP/IP with stationary random losses, Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, p.231-242, August 28-September 01, 2000, Stockholm, Sweden
• 7: F. Baccelli, D. McDonald, and J. Reynier. A Mean-field Model for Multiple TCP Connections through a Buffer. In Proceedings of IFIP WG 7.3 Performance, 2002.
• 8: Tian Bu , Don Towsley, Fixed point approximations for TCP behavior in an AQM network, Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems, p.216-225, June 2001, Cambridge, Massachusetts, United States
• 9: J. W. Daniel and R. E. Moore, editors. Computation and theory in ordinary differential equations. San Francisco, W. H. Freeman, 1970.
• 10: Kevin Fall , Sally Floyd, Simulation-based comparisons of Tahoe, Reno and SACK TCP, ACM SIGCOMM Computer Communication Review, v.26 n.3, p.5-21, July 1996 [doi>10.1145/235160.235162]
• 11: Sally Floyd , Van Jacobson, Random early detection gateways for congestion avoidance, IEEE/ACM Transactions on Networking (TON), v.1 n.4, p.397-413, Aug. 1993 [doi>10.1109/90.251892]
• 12: C. Hollot, Y. Liu, V. Misra, and D. Towsley. Unresponsive flows and AQM performance. In Proceedings of IEEE/INFOCOM, 2003.
• 13: C. Hollot, V. Misra, D. Towsley, and W.-B. Gong. On Designing Improved Controllers for AQM Routers Supporting TCP Flows. In Proceedings of IEEE/INFOCOM, April 2001.
• 14: Jitendra Padhye , Victor Firoiu , Don Towsley , Jim Kurose, Modeling TCP throughput: a simple model and its empirical validation, Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication, p.303-314, August 31-September 04, 1998, Vancouver, British Columbia, Canada
• 15: Srisankar Kunniyur , R. Srikant, Analysis and design of an adaptive virtual queue (AVQ) algorithm for active queue management, Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications, p.123-134, August 2001, San Diego, California, United States
• 16: Vishal Misra , Wei-Bo Gong , Don Towsley, Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED, Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, p.151-160, August 28-September 01, 2000, Stockholm, Sweden
• 17: Konstantinos Psounis , Rong Pan , Balaji Prabhakar , Damon Wischik, The scaling hypothesis: simplifying the prediction of network performance using scaled-down simulations, ACM SIGCOMM Computer Communication Review, v.33 n.1, p.35-40, January 2003 [doi>10.1145/774763.774768]
• 18: P. Tinnakornsrisuphap and A. Makowski. Limit Behavior of ECN/RED Gateways Under a Large Number of TCP Flows. In Proceedings of IEEE Infocom, 2003.

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.0 General

Additional Classification:
  C. Computer Systems Organization
  C.4 PERFORMANCE OF SYSTEMS
      Subjects: Modeling techniques

General Terms:
Performance

Keywords:
fluid model, large-scale IP networks, simulation