PathAB: A New Method to Estimate End-to-End Available Bandwidth of Network Path

Estimating available bandwidth accurately is extremely important for many network related applications, especially the ones which need real-time traffic information. With the ever increasing use of Internet, several available bandwidth measurement techniques have been proposed. But most of them assume fluid traffic model, whereas studies show that current Internet traffic follows Poisson distribution. Moreover, very few can operate in stand-alone mode and have relatively high estimation errors. We propose a new method, PathAB, which combines the concepts of three existing algorithms, MoSeab, PoissonProb and PathChirp. It first obtains a rough estimation of available bandwidth using an exponential probing train, and later obtains the final estimate using several Poisson distributed probing trains. It can operate both in client-server and stand-alone modes. Unlike other stand-alone methods, PathAB sends very small echo packets back-to-back after the large probe packets to reduce the cross-traffic effect in returning path as well as the estimation error

Network Independent Available Bandwidth Sampling and Measurement

Abstract. Available bandwidth knowledge is very useful to network protocols. Unfortunately, available bandwidth is also very difficult to measure in packet networks, where methods to guarantee and keep track of the bandwidth (eg, weighted fair queuing scheduling) do not work well, for example the Internet. In this paper we are dealing with an available bandwidth sampling technique based on the observation of packet time dispersion in a packet train or pair. In standard techniques the available bandwidth is sampled by using a “bytes divided by dispersion ” (or “bytes over time”, BoT) calculation and then filtered. This method of calculating available bandwidth samples has been used in all packet dispersion related work. We propose a new sampling method of available bandwidth called ab-probe. The ab-probe method uses an intuitive model that helps understand and correct the error introduced by the BoT sample calculation. We theoretically compare the new model with the previous one, exploring their differences, observability and robustness. We argue that the model may significantly improve protocols that can use an available bandwidth measurement, in particular transport-level protocols that currently use the BoT calculation.