Large scale probabilistic available bandwidth estimation

The common utilization-based definition of available bandwidth and many of the existing tools to estimate it suffer from several important weaknesses: i) most tools report a point estimate of average available bandwidth over a measurement interval and do not provide a confidence interval; ii) the commonly adopted models used to relate the available bandwidth metric to the measured data are invalid in almost all practical scenarios; iii) existing tools do not scale well and are not suited to the task of multi-path estimation in large-scale networks; iv) almost all tools use ad-hoc techniques to address measurement noise; and v) tools do not provide enough flexibility in terms of accuracy, overhead, latency and reliability to adapt to the requirements of various applications. In this paper we propose a new definition for available bandwidth and a novel framework that addresses these issues. We define probabilistic available bandwidth (PAB) as the largest input rate at which we can send a traffic flow along a path while achieving, with specified probability, an output rate that is almost as large as the input rate. PAB is expressed directly in terms of the measurable output rate and includes adjustable parameters that allow the user to adapt to different application requirements. Our probabilistic framework to estimate network-wide probabilistic available bandwidth is based on packet trains, Bayesian inference, factor graphs and active sampling. We deploy our tool on the PlanetLab network and our results show that we can obtain accurate estimates with a much smaller measurement overhead compared to existing approaches.Comment: Submitted to Computer Network

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

Greedy Gossip with Eavesdropping

This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.Comment: 25 pages, 7 figure

Optimization and Analysis of Distributed Averaging with Short Node Memory

In this paper, we demonstrate, both theoretically and by numerical examples, that adding a local prediction component to the update rule can significantly improve the convergence rate of distributed averaging algorithms. We focus on the case where the local predictor is a linear combination of the node's two previous values (i.e., two memory taps), and our update rule computes a combination of the predictor and the usual weighted linear combination of values received from neighbouring nodes. We derive the optimal mixing parameter for combining the predictor with the neighbors' values, and carry out a theoretical analysis of the improvement in convergence rate that can be obtained using this acceleration methodology. For a chain topology on n nodes, this leads to a factor of n improvement over the one-step algorithm, and for a two-dimensional grid, our approach achieves a factor of n^1/2 improvement, in terms of the number of iterations required to reach a prescribed level of accuracy

Red blood cell transfusion and increased length of storage are not associated with deep vein thrombosis in medical and surgical critically ill patients: a prospective observational cohort study

Abstract Introduction With prolonged storage times, cell membranes of red blood cells (RBCs) undergo morphologic and biochemical changes, termed 'RBC storage lesions'. Storage lesions may promote inflammation and thrombophilia when transfused. In trauma patients, RBC transfusion was an independent risk factor for deep vein thrombosis (DVT), specifically when RBC units were stored > 21 days or when 5 or more units were transfused. The objective of this study was to determine if RBC transfusions or RBC storage age predicts incident DVT in medical or surgical intensive care unit (ICU) patients. Methods Using a database which prospectively enrolled 261 patients over the course of 1 year with an ICU stay of at least 3 days, we analyzed DVT and RBC transfusions using Cox proportional hazards regression. Transfusions were analyzed with 4 thresholds, and storage age using 3 thresholds. DVTs were identified by twice-weekly proximal leg ultrasounds. Multivariable analyses were adjusted for 4 significant DVT predictors in this population (venous thrombosis history, chronic dialysis, platelet transfusion and inotropes). Results Of 261 patients, 126 (48.3%) had at least 1 RBC transfusion; 46.8% of those transfused had ≥ 5 units in ICU. Patients receiving RBCs were older (68.8 vs 64.1 years), more likely to be female (47.0 vs 30.7), sicker (APACHEII 26.8 vs 24.4), and more likely to be surgical (21.4 vs 8.9) (P 7 days, ≤ 14 or > 14 days, ≤ 21 or > 21 days). Among patients transfused, no multivariable analyses showed that RBC transfusion or storage age predicted DVT. Trends were counter to the hypothesis (e.g., RBC storage for ≤ 7 days suggested a higher DVT risk compared to > 7 days (HR 5.3; 95% CI 1.3-22.1). Conclusions We were unable to detect any association between RBC transfusions or prolonged red cell storage and increased risk of DVT in medical or surgical ICU patients. Alternate explanations include a lack of sufficient events or patients' interaction, between patient groups, a mixing of red cell storage times creating differential effects on DVT risk, and unmeasured confounders