Internet traffic exhibits multifaceted burstiness and correlation structure over a wide span of time scales. Previous work analyzed this structure in terms of heavy-tailed session characteristics, as well as TCP timeouts and congestion avoidance, in relatively long time scales. We focus on shorter scales, typically less than 100-1000 milliseconds. Our objective is to identify the actual mechanisms that are responsible for creating bursty traffic in those scales. We show that TCP self-clocking, joint with queueing in the network, can shape the packet interarrivals of a TCP connection in a two-level ON-OFF pattern. This structure creates strong correlations and burstiness in time scales that extend up to the Round-Trip Time (RTT) of the connection. This effect is more important for bulk transfers that have a large bandwidth-delay product relative to their window size. Also, the aggregation of many flows, without rescaling their packet interarrivals, does not converge to a Poisson stream, as one might expect from classical superposition results. Instead, the burstiness in those scales can be significantly reduced by TCP pacing. In particular, we focus on the importance of the minimum pacing timer, and show that a 10-millisecond timer would be too coarse for removing short-scale traffic burstiness, while a 1-millisecond timer would be sufficient to make the traffic almost as smooth as a Poisson stream in sub-RTT scales.
Abstract-The available bandwidth (avail-bw) of a network path is an important performance metric and its end-to-end estimation has recently received significant attention. Previous work focused on the estimation of the average avail-bw, ignoring the significant variability of this metric in different time scales. In this paper, we show how to estimate a given percentile of the avail-bw distribution at a user-specified time scale. If two estimated percentiles cover the bulk of the distribution (say 10% to 90%), the user can obtain a practical estimate for the avail-bw variation range. We present two estimation techniques. The first is iterative and non-parametric, meaning that it is more appropriate for very short time scales (typically less than 100ms), or in bottlenecks with limited flow multiplexing (where the avail-bw distribution may be non-Gaussian). The second technique is parametric, because it assumes that the avail-bw follows the Gaussian distribution, and it can produce an estimate faster because it is not iterative. The two techniques have been implemented in a measurement tool called Pathvar. Pathvar can track the avail-bw variation range within 10-20%, even under non-stationary conditions. We identify four factors that play a crucial role in the variation range of the avail-bw: traffic load, number of competing flows, rate of competing flows, and of course the measurement time scale. Finally, we present a new way to detect whether a probing rate is larger than the avail-bw, without relying on the fluid traffic assumption or on static thresholds.
Internet applications and users have very diverse service expectations, making the current same-service-to-all model inadequate and limiting. In the relative differentiated services approach, the network traffic is grouped in a small number of service classes which are ordered based on their packet forwarding quality , in terms of per-hop metrics for the queueing delays and packet losses. The users and applications, in this context, can adaptively choose the class that best meets their quality and pricing constraints, based on the assurance that higher classes will be better, or at least no worse, than lower classes . In this work, we propose the proportional differentiation model as a way to refine and quantify this basic premise of relative differentiated services. The proportional differentiation model aims to provide the network operator with the ' tuning knobs ' for adjusting the quality spacing between classes, independent of the class loads ; this cannot be achieved with other relative differentiation models, such as strict prioritization or capacity differentiation. We apply the proportional model on queueing-delay differentiation only, leaving the problem of coupled delay and loss differentiation for future work. We discuss the dynamics of the proportional delay differentiation model and state the conditions under which it is feasible . Then, we identify and evaluate (using simulations) two packet schedulers that approximate the proportional differentiation model in heavy-load conditions, even in short timescales. Finally, we demonstrate that such per-hop and class-based mechanisms can provide consistent end-to-end differentiation to individual flows from different classes, independently of the network path and flow characteristics.
The available bandwidth (avail-bw) in a network path is of major importance in congestion control, streaming applications, QoS verification, server selection, and overlay networks. We describe an end-to-end methodology, called Self-Loading Periodic Streams (SLoPS), for measuring avail-bw. The basic idea in SLoPS is that the one-way delays of a periodic packet stream show an increasing trend when the stream's rate is higher than the avail-bw. We implemented SLoPS in a tool called pathload. The accuracy of the tool has been evaluated with both simulations and experiments over real-world Internet paths. Pathload is non-intrusive, meaning that it does not cause significant increases in the network utilization, delays, or losses. We used pathload to evaluate the variability ('dynamics') of the avail-bw in some paths that cross USA and Europe. The avail-bw becomes significantly more variable in heavily utilized paths, as well as in paths with limited capacity (probably due to a lower degree of statistical multiplexing). We finally examine the relation between avail-bw and TCP throughput. A persistent TCP connection can be used to roughly measure the avail-bw in a path, but TCP saturates the path, and increases significantly the path delays and jitter.
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