Knowing the largest rate at which data can be sent on an end-to-end path such that the egress rate is equal to the ingress rate with high probability can be very practical when choosing transmission rates in video streaming or selecting peers in peer-to-peer applications. We introduce probabilistic available bandwidth, which is defined in terms of ingress rates and egress rates of traffic on a path, rather than in terms of capacity and utilization of the constituent links of the path like the standard available bandwidth metric. In this paper, we describe a distributed algorithm, based on a probabilistic graphical model and Bayesian active learning, for simultaneously estimating the probabilistic available bandwidth of multiple paths through a network. Our procedure exploits the fact that each packet train provides information not only about the path it traverses, but also about any path that shares a link with the monitored path. Simulations and PlanetLab experiments indicate that this process can dramatically reduce the number of probes required to generate accurate estimates.
Other approaches have been proposed previously to perform network-wide estimations without overloading the network and or consuming a large amount of resources. Song and Yalagandula proposed @math to measure real-time end-to-end network properties such as latency and loss rate @cite_5 . They measure only a subset of the network, by choosing paths according to the observed load on the links and at the end nodes, to infer statistics about the entire network. However, their work does not address the problem of estimating available bandwidth. @cite_16 , Hu and Steenkiste introduce BRoute; a scalable available bandwidth estimation system based on route sharing. They assume that most Internet bottlenecks are on path edges and use the fact that links near end-nodes are often shared by many paths. Although some of the intuition behind BRoute and our work is similar, the core implementation differs; it is not based on a probabilistic framework.
@cite_5 @cite_16
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Measuring real-time end-to-end network path performance metrics is important for several distributed applications such as media streaming systems (e.g., for switching to paths with higher bandwidth and lower jitter) and content distribution systems (e.g., for selecting servers with lower latency). However, it is challenging to perform such end-to-end pairwise measurements in large distributed systems while achieving high accuracy and avoid interfering with existing traffic. On the end hosts, the measurements can overload the machine by causing interference among themselves and other processes. On the network, the measurement packets from different hosts can interfere among themselves and with other flows on bottleneck links. In this paper, we propose a system to monitor end-host and network resources and adapt the number of measurements according to the observed load. Our scheme avoids interference by measuring only a small subset of network paths and reconstructing the entire network path properties from the partial, indirect measurements. Our simulation experiments and real testbed experiments on PlanetLab show that our path selection algorithm working with resource constraints does not adversely affect the accuracy of inference and our system can effectively adapt to the changing resource usage at the end hosts. 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 .