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.
Recent progress in active measurement techniques has made it possible to estimate end-to-end path available bandwidth. However, how to efficiently obtain available bandwidth information for the N2 paths in a large N-node system remains an open problem. While researchers have developed coordinate-based models that allow any node to quickly and accurately estimate latency in a scalable fashion, no such models exist for available bandwidth. In this paper we introduce BRoute--a scalable available bandwidth estimation system that is based on a route sharing model. The characteristics of BRoute are that its overhead is linear with the number of end nodes in the system, and that it requires only limited cooperation among end nodes. BRoute leverages the fact that most Internet bottlenecks are on path edges, and that edges are shared by many different paths. It uses AS-level source and sink trees to characterize and infer path-edge sharing in a scalable fashion. In this paper, we describe the BRoute architecture and evaluate the performance of its components. Initial experiments show that BRoute can infer path edges with an accuracy of over 80 . In a small case study on Planetlab, 80 of the available bandwidth estimates obtained from BRoute are accurate within 50 . In recent years the research community has developed many techniques to estimate the end-to-end available bandwidth of an Internet path. This important metric can be potentially exploited to optimize the performance of several distributed systems and, even, to improve the effectiveness of the congestion control mechanism of TCP. Thus, it has been suggested that some existing estimation techniques could be used for this purpose. However, existing tools were not designed for large-scale deployments and were mostly validated in controlled settings, considering only one measurement running at a time. In this paper, we argue that current tools, while offering good estimates when used alone, might not work in large-scale systems where several estimations severely interfere with each other. We analyze the properties of the measurement paradigms employed today and discuss their functioning, study their overhead and analyze their interference. Our testbed results show that current techniques are insufficient as they are. Finally, we will discuss and propose some principles that should be taken into account for including available bandwidth measurements in large-scale distributed systems. On-line, spatially localized information about internal network performance can greatly assist dynamic routing algorithms and traffic transmission protocols. However, it is impractical to measure network traffic at all points in the network. A promising alternative is to measure only at the edge of the network and infer internal behavior from these measurements. In this paper we concentrate on the estimation and localization of internal delays based on end-to-end delay measurements from a source to receivers. We propose a sequential Monte Carlo (SMC) procedure capable of tracking nonstationary network behavior and estimating time-varying, internal delay characteristics. Simulation experiments demonstrate the performance of the SMC approach. This paper presents a new bandwidth inference mechanism that can be used to predict bandwidth across two nodes on the Internet. We used simulation and actual implementation on PlanetLab to compare the performance of the proposed mechanism against an existing approach. The results indicate that our approach is lightweight and yields better performance. With the ever growing size of the Internet and increasing popularity of the overlay and peer-to-peer networks, scalable end-to-end (e2e) network monitoring is essential for better network management and application performance. For large scale networks, an e2e monitoring infrastructure should minimize the measurement cost while ensuring that the network is still monitored at fine enough time-scales required for each application flow. We explore the relationships between different e2e network metrics with the aim of leveraging such relationships for reducing monitoring costs while maintaining measurement accuracy. We analyze long range network measurements from PlanetLab, where we collected e2e network data (route, number of hops, capacity bandwidth and available bandwidth) for about two years on several thousand paths. We also present a few schemes to leverage the metric correlations and reduce the monitoring cost. Our preliminary results indicate that in some cases, we can reduce the monitoring costs by 75 while maintaining the accuracy at about 88 .