In general, the traffic characteristics of the individual applications that constitute the aggregate traffic on a network can be important for capacity engineering. In this paper, we demonstrate based on mathematical analysis the value of application specific measurements even when there is no service differentiation. In other words, under certain assumptions, we obtain the result that errors in engineering can occur, and in particular, under-engineering can occur when traffic characteristics of individual applications are ignored. The assumptions are that the individual applications can be modeled adequately as Fractional Brownian Motions and that measurements are available only at relatively coarse time scales. The results in this paper emphasize the value of collecting fine-grained traffic measurements.

Recent measurement and simulation studies have revealed that wide area network traffic has complex statistical, possibly multifractal, characteristics on short timescales, and is self-similar on long timescales. In this paper, using measured TCP traces and queueing simulations, we show that the fine timescale features can affect performance substantially at low and intermediate utilizations, while the coarse timescale self-similarity is important at intermediate and high utilizations. We outline an analytical method for estimating performance for traffic that is self-similar on coarse timescales and multi-fractal on fine timescales, and show that the engineering problem of setting safe operating points for planning or admission control can be significantly affected by fine timescale fluctuations in network traffic.

Recent traffic analyses from various packet networks have shown the existence of long-range dependence in bursty traffic. In evaluating its impact on queuing performance, earlier investigations have noted how the presence of long-range dependence, or a high value of the Hurst parameter H, is often associated with surprisingly large queue sizes. As a result, a common impression has been created of expecting queuing performance to be worse as H increases, but this impression can be misleading. In fact, there are examples in which larger values of H are associated with smaller queues. So the question is how can one tell whether queuing performance would improve or degrade as H rises? In this paper, we show that the relative queuing performance can be assessed by identifying a couple of time scales. First, in comparing a high-H process with a low-H process, there is a unique time scale tm at which the variances of the two processes match (assuming exact, second-order self similarity for both processes). Second, there are time scales tqi that are most relevant for queuing the arrivals of process i. If both of the queuing scales tqi exceed the variance-matching scale tm, then the high-H queue is worse; if the queuing scales are smaller, then the low-H queue is worse. However, no firm prediction can be made in the remaining case of tm falling between the two queuing scales. Numerical examples are given to demonstrate our results.

Most of the studies of feedback-based flow and congestion control consider only persistent sources which always have data to send. However, with the rapid growth of Internet applications built on TCP/IP such as the World Wide Web and the standardization of traffic management schemes such as Available Bit Rate (ABR) in Asynchronous Transfer Mode (ATM) networks, it is essential to evaluate the performance of feedback-based protocols using traffic models which are specific to dominant applications. This paper presents a method for analysing feedback-based protocols with a Web-user-like input traffic where the source alternates between "transfer" periods followed by "think" periods. Our key results, which are presented for the TCP protocol, are:(1) The goodputs and the fraction of time that the system has some given number of transferring sources are insensitive to the distributions of transfer (file or page) sizes and think times except through the ratio of their means. Thus, apart from network round-trip times, only the ratio of average transfer sizes and think times of users need be known to size the network for achieving a specific quality of service.(2) The Engset model can be adapted to accurately compute goodputs for TCP and TCP over ATM, with different buffer management schemes. Though only these adaptations are given in the paper, the method based on the Engset model can be applied to analyze other feedback systems, such as ATM ABR, by finding a protocol specific adaptation. Hence, the method we develop is useful not only for analysing TCP using a source model significantly different from the commonly used persistent sources, but also can be useful for analysing other feedback schemes.(3) Comparisons of simulated TCP traffic to measured Ethernet traffic shows qualitatively similar autocorrelation when think times follow a Pareto distribution with infinite variance. Also, the simulated and measured traffic have long range dependence. In this sense our traffic model, which purports to be Web-user-like, also agrees with measured traffic.

This paper proposes the use of a "drop from front" scheme for improving TCP performance in high bandwidth-delay product networks. In particular, for "TCP over ATM" we compare the performance when drop from front is used at the output port of ATM switches with the performance under tail drop, its variations, and with variations of random early detection (RED). In drop from front, when a cell arrives at a full buffer, the cell closest to being transmitted is dropped, thus creating space for the arriving cell. This policy causes duplicate acknowledgements to be sent one whole buffer drain time earlier than is the case under tail drop. These quicker duplicate acknowledgements cause TCP with fast retransmit to recognize losses faster and invoke congestion control actions earlier than would be the case under tail drop. This earlier reaction translates into considerable performance improvement. Hence, drop from front successfully utilizes the ability of TCP with fast retransmit to quickly recognize and react to congestion information (at the third repeat acknowledgement, as opposed to time-out). Roughly, the earlier action by the sources causes the congestion not to grow quite as severe, which prevents later over-reaction by the sources, and thus increases throughput.

No abstract available.