Applications such as traffic engineering and network provisioning can greatly benefit from knowing, in real time, what is the largest input rate at which it is possible to transmit on a given path without causing congestion. We consider a probabilistic formulation for available bandwidth where the user specifies the probability of achieving an output rate almost as large as the input rate. We are interested in estimating and tracking the network-wide probabilistic available bandwidth (PAB) on multiple paths simultaneously with minimal overhead on the network. We propose a novel framework based on chirps, Bayesian inference, belief propagation and active sampling to estimate the PAB. We also consider the time evolution of the PAB by forming a dynamic model and designing a tracking algorithm based on particle filters. We implement our method in a lightweight and practical tool that has been deployed on the PlanetLab network to do online experiments. We show through these experiments and simulations that our approach outperforms block-based algorithms in terms of input rate cost and probability of successful transmission.
In this paper, we propose a practical, efficient and real-time technique to estimate the end-to-end available bandwidth in the condition of probe packets losing. One of main ideas of the proposed technology is compensation mechanism, which uses a least square method to approximately compensate the losing packets based on polynomial-mimesis technology in the communication channel. The second is that it uses the relationship between utilization and the sending rate of the probe packet when each probe packet needs to line up. The last one is that paper uses Kalman Filter to rebuild the linear system between utilization and rate, and then calculate the available bandwidth. Theoretical analysis and experimental results show that the proposed technique is effective. In this paper we address the problem of online bandwidth estimation in wired and wireless LANs. To this end, we employ active probing, i.e. we continuously inject packet probes into the network. We present the key challenges and analyze the trade-offs between fast change detection and estimate smoothness. We show the benefit of using Kalman filtering to obtain optimal estimates under certain conditions and provide a procedure for parameterizing the filter with respect to specific use cases. Furthermore, we evaluate the influence of probing train length on the results. Based on our findings we developed a tool implementing the presented methodology. We support our theoretical results by a number of real-world measurements as well as simulations. This paper presents a filter-based method BART (Bandwidth Available in Real-Time) for real-time estimation of end-to-end available bandwidth in packet-switched communication networks. BART relies on self-induced congestion, and repeatedly samples the available bandwidth of the network path with sequences of probe-packet pairs. The method is light-weight with respect to computation and memory requirements, and performs well when only a small amount of probe traffic is injected. BART uses Kalman filtering, which enables real-time estimation. It maintains a current estimate, which is incrementally improved with each new measurement of the inter-packet time separation in a sequence of probe-packet pairs. It is possible to tune BART according to specific needs. The estimation performance can be significantly enhanced by employing a change-detection technique. An implementation of BART has been evaluated in a physical test network with carefully controlled cross traffic. In addition, experiments have been performed over the Internet as well as over a mobile broadband connection. The available bandwidth (AB) of an end-to-end path is its remaining capacity and it is an important metric for several applications. That's why several available bandwidth estimation tools have been published recently. Most of these tools use the probe rate model. This model is based on the concept of self- induced congestion and requires that the tools send a packet train at a rate matching the available bandwidth. The main issue with this model is that these tools congest the path under study. In this paper we present a novel available bandwidth estimation tool that takes into account this issue. Our tool is based on a mathematical model that sends packet trains at a rate lower than the AB. The main drawback of this model is that it is not able to track the AB. To solve this issue we propose to apply Kalman filters (KF) to the model. By applying these filters we can produce real-time estimations of the available bandwidth and monitor its changes. In addition the KFs are able to filter the noisy (erroneous) measurements improving the overall accuracy. We also present an extensive evaluation of our tool in different network scenarios and we compare its performance with that of pathChirp (a state-of-the-art available bandwidth estimation tool). This paper presents a new method, BART (Bandwidth Available in Real-Time), for estimating the end-to-end available bandwidth over a network path. It estimates bandwidth quasi-continuously, in real-time. The method has also been implemented as a tool. It relies on self-induced congestion, and repeatedly samples the available bandwidth of the network path with sequences of probe packet pairs, sent at randomized rates. BART requires little computation in each iteration, is light-weight with respect to memory requirements, and adds only a small amount of probe traffic. The BART method uses Kalman filtering, which enables real-time estimation (a.k.a. tracking). It maintains a current estimate, which is incrementally improved with each new measurement of the inter-packet time separations in a sequence of probe packet pairs. The measurement model has a strong non-linearity, and would not at first sight be considered suitable for Kalman filtering, but we show how this non-linearity can be handled. BART may be tuned according to the specific needs of the measurement application, such as agility vs. stability of the estimate. We have tested an implementation of BART in a physical test network with carefully controlled cross traffic, with good accuracy and agreement. Test measurements have also been performed over the Internet. We compare the performance of BART with that of pathChirp, a state-of-the-art tool for measuring end-to-end available bandwidth in real-time. End-to-end available bandwidth estimation is very important for bandwidth dependent applications, quality of service verification and traffic engineering. Although several techniques and tools have been developed in the past, producing reliable estimations in real-time still remains challenging -- it is necessary to ensure that the measurement process is accurate, non-intrusive and robust to non-deterministic delays or traffic bursts. This paper presents ASSOLO, a new active probing tool for estimating available bandwidth based on the concept of self-induced congestion''. ASSOLO features a new probing traffic profile called (Reflected ExponentiAl Chirp), which tests a wide range of rates being more accurate in the center of the probing interval. Moreover, the tool runs inside a real-time operating system and uses some de-noising techniques to improve the measurement process. Experimental results show that ASSOLO outperforms pathChirp, a state-of-the-art measurement tool, estimating available bandwidth with greater accuracy and stability.