End-to-end estimation of the available bandwidth variation range

Abstract

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.

Laboratory-based calibration of available bandwidth estimation tools

“…Finally, our calibration study has focused on average AB over a time interval. PATHLOAD reports a variation range for AB, and Jain et al [17,31] have shown that the variation of AB is an important measurement target. Extending our calibration study to consider AB variation is a subject for future work.…”

A Min-Plus System Interpretation of Bandwidth Estimation

“…More recently proposed schemes, including pathload [4], [5], pathvar [9], TOPP [3], PTR/IGI [6], pathchirp [7], and BFind [8], adaptively vary the rate of probing traffic to induce congestion in the network. This has been found to increase the fidelity of estimation methods.…”

“…The benefits of knowing how much network bandwidth is available to an application has motivated the development of techniques that infer the available bandwidth from traffic measurements [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]. Even though the number of techniques available today is significant and much empirical experience has been gained, less progress has been made towards a foundational understanding of measurement based methods for estimating the available bandwidth.…”

Measuring Bandwidth Signatures of Network Paths

“…This case is typically the hardest to estimate since a tight high-speed link implies that its utilization needs to be very large, at least 90% in all our cases. It has been shown in [10] that as the utilization of the tight link increases, so does the variation in the average available bandwidth, the metric that we are measuring. Cases 2,3, and 4 are designed to stress-test our estimation methodology in heavily loaded network paths.…”

“…This link is referred to as the tight link. Tools to estimate the end-to-end available bandwidth of a network path, defined as the available bandwidth at the tight link, have been proposed in [7,22,9,18,19,26,14,10].…”