Browsing by Author "Willinger, Walter"

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    On the impact of variability on the buffer dynamics in IP networks
    (1999-09-20) Joo, Youngmi; Ribeiro, Vinay Joseph; Feldmann, Anja; Gilbert, Anna; Willinger, Walter; Center for Multimedia Communications (http://cmc.rice.edu/)
    The main objective of this paper is to demonstrate in the context of a simple TCP/IP-based network that depending on the underlying assumptions about the inherent nature of the variability of network traffic, very different conclusions can be derived for a number of well-studied and apparently well-understood problems in the areas of traffic engineering and management. For example, by either fully ignoring or explicitly accounting for the empirically observed variability of network traffic at the source level, we provide detailed ns-2-based simulation results for two commonly-used traffic workload scenarios that can give rise to fundamentally different buffer dynamics in IP routers. We also discuss a set of ns-2 simulation experiments to illustrate that the queuing dynamics within IP routers can be qualitatively very different depending on whether the observed variability of measured network traffic over small time scales is assumed to be in part endogenous in nature (i.e., due to TCP's feedback flow control mechanism, which is "closed loop") or is exogenously determined, resulting in an "open loop" characterization of network traffic arriving at the routers.
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    TCP/IP traffic dynamics and network performance: A lesson in workload modeling, flow control and trace-driven simulations
    (2001-04-20) Joo, Youngmi; Ribeiro, Vinay Joseph; Feldmann, Anja; Gilbert, Anna; Willinger, Walter; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)
    The main objective of this paper is to demonstrate in the context of a simple TCP/IP-based network that depending on the underlying assumptions about the inherent nature of the dynamics of network traffic, very different conclusions can be derived for a number of well-studied and apparently well-understood problems in the area of performance evaluation. For example, a traffic workload model can either completely ignore the empirically observed high variability at the TCP connection level (i.e., assume "infinite sources") or explicitly account for it with the help of heavy-tailed distributions for TCP connection sizes or durations. Based on detailed ns-2 simulations results, we illustrate that these two commonly-used traffic workload scenarios can give rise to fundamentally different buffer dynamics in IP routers. Using a second set of ns-2 simulation experiements, we also illustrate a qualitatively very different queueing behavior within IP routers depending on whether the traffic arriving at the router is assumed to be endogenous in nature (i.e., a result of the "closed loop" nature of the feedback-based congestion control algorithm of TCP) or exogenously determined (i.e., given by some conventional traffic model - a fixed "open loop" description of the traffic as seen by the router).
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    Toward an Improved Understanding of Network Traffic Dynamics
    (Wiley, 2000-01-15) Riedi, Rudolf H.; Willinger, Walter; Park and Willinger; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)
    Since the discovery of long range dependence in Ethernet LAN traces there has been significant progress in developing appropriate mathematical and statistical techniques that provide a physical-based, networking-related understanding of the observed fractal-like or self-similar scaling behavior of measured data traffic over time scales ranging from hundreds of milliseconds to seconds and beyond. These developments have helped immensely in demystifying fractal-based traffic modeling and have given rise to new insights and physical understanding of the effects of large-time scaling properties in measured network traffic on the design, management and performance of high-speed networks. However, to provide a complete description of data network traffic, the same kind of understanding is necessary with respect to the dynamic nature of traffic over small time scales, from a few hundreds of milliseconds downwards. Because of the predominant protocols and end-to-end congestion control mechanisms that determine the flow of packets, studying the fine-time scale behavior or local characteristics of data traffic is intimately related to understanding the complex interactions that exist in data networks. In this chapter, we first summarize the results that provide a unifying and consistent picture of the large-time scaling behavior of data traffic. We then report on recent progress in studying the small-time scaling behavior in data network traffic and outline a number of challenging open problems that stand in the way of providing an understanding of the local traffic characteristics that is as plausible, intuitive, appealing and relevant as the one that has been found for the global or large-time scaling properties of data traffic.
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    Toward an Improved Understanding of Network Traffic Dynamics
    (1999-06-20) Riedi, Rudolf H.; Willinger, Walter; Digital Signal Processing (http://dsp.rice.edu/)
    Since the discovery of long range dependence in Ethernet LAN traces there has been significant progress in developing appropriate mathematical and statistical techniques that provide a physical-based, networking-related understanding of the observed fractal-like or self-similar scaling behavior of measured data traffic over time scales ranging from hundreds of milliseconds to seconds and beyond. These developments have helped immensely in demystifying fractal-based traffic modeling and have given rise to new insights and physical understanding of the effects of large-time scaling properties in measured network traffic on the design, management and performance of high-speed networks. However, to provide a complete description of data network traffic, the same kind of understanding is necessary with respect to the dynamic nature of traffic over small time scales, from a few hundreds of milliseconds downwards. Because of the predominant protocols and end-to-end congestion control mechanisms that determine the flow of packets, studying the fine-time scale behavior or local characteristics of data traffic is intimately related to understanding the complex interactions that exist in data networks. In this chapter, we first summarize the results that provide a unifying and consistent picture of the large-time scaling behavior of data traffic. We then report on recent progress in studying the small-time scaling behavior in data network traffic and outline a number of challenging open problems that stand in the way of providing an understanding of the local traffic characteristics that is as plausible, intuitive, appealing and relevant as the one that has been found for the global or large-time scaling properties of data traffic.