1,433 research outputs found
MR-BART: Multi-Rate Available Bandwidth Estimation in Real-Time
In this paper, we propose Multi-Rate Bandwidth Available in Real Time
(MR-BART) to estimate the end-to-end Available Bandwidth (AB) of a network
path. The proposed scheme is an extension of the Bandwidth Available in Real
Time (BART) which employs multi-rate (MR) probe packet sequences with Kalman
filtering. Comparing to BART, we show that the proposed method is more robust
and converges faster than that of BART and achieves a more AB accurate
estimation. Furthermore, we analyze the estimation error in MR-BART and obtain
analytical formula and empirical expression for the AB estimation error based
on the system parameters.Comment: 12 Pages (Two columns), 14 Figures, 4 Tables
The Quest for Bandwidth Estimation Techniques for large-scale Distributed Systems
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 has been proposed for use in several distributed systems and, more recently, has even been considered to improve 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. 1
Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning
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
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