A loss-event driven scalable fluid simulation method for high-speed networks

Increase of size and bandwidth of computer network posed a research challenge to evaluate proposed TCP/IP protocol and corresponding queuing policies in this scenario. Simulation provides an easier and cheaper method to evaluate TCP proposals and queuing disciplines as compared to experiment with real hardware. In this paper, problem associated with scalability of current simulation method for high-speed network case is discussed. Hence, we present a scalable time-adaptive numerical simulation driven by loss events to represent dynamics of high-speed networks using fluid-based models. The new method uses a loss event to dynamically adjust the size of a time step for a numerical solver which solves a system of differential equations representing dynamics of protocols and nodes' behaviors. A numerical analysis of the proposed protocol is discussed. A simple simulation of high-speed TCP variants is presented using our method. The simulation results and analysis show that the time-adaptive method reduces computational time while achieving the same accuracy compared to that of a fixed step-size method.

[1]  Sally Floyd,et al.  HighSpeed TCP for Large Congestion Windows , 2003, RFC.

[2]  Suman Kumar,et al.  On Transport Protocol Performance Measurement over 10Gbps High Speed Optical Networks , 2009, 2009 Proceedings of 18th International Conference on Computer Communications and Networks.

[3]  Heng-Qing Ye,et al.  Stability of data networks under an optimization-based bandwidth allocation , 2003, IEEE Trans. Autom. Control..

[4]  Vishal Misra,et al.  Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED , 2000, SIGCOMM.

[5]  François Baccelli,et al.  Flow level simulation of large IP networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[6]  Douglas J. Leith,et al.  H-TCP : TCP for high-speed and long-distance networks , 2004 .

[7]  Steven H. Low,et al.  Optimization flow control—I: basic algorithm and convergence , 1999, TNET.

[8]  Wei-Bo Gong,et al.  Time-driven fluid simulation for high-speed networks , 1999, IEEE Trans. Inf. Theory.

[9]  Donald F. Towsley,et al.  Fluid models and solutions for large-scale IP networks , 2003, SIGMETRICS '03.

[10]  A. Iserles.  A First Course in the Numerical Analysis of Differential Equations: Stiff equations , 2008 .

[11]  Vishal Misra,et al.  Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED , 2000, SIGCOMM 2000.

[12]  Cheng Jin,et al.  FAST TCP: Motivation, Architecture, Algorithms, Performance , 2006, IEEE/ACM Transactions on Networking.

[13]  Injong Rhee,et al.  Binary increase congestion control (BIC) for fast long-distance networks , 2004, IEEE INFOCOM 2004.

[14]  Tom Kelly,et al.  Scalable TCP: improving performance in highspeed wide area networks , 2003, CCRV.