Left ventricular hypertrophy has been found to be associated with a reduction of coronary vascular reserve, which could be responsible for episodes of myocardial ischemia. To evaluate coronary flow and resistance reserve in patients with chronic aortic regurgitation, coronary sinus blood flow and coronary resistance were measured before and after an intravenous dipyridamole infusion (0.14 mg/kg per min X 4 min) in eight control subjects and eight patients with aortic regurgitation, exertional angina pectoris and normal coronary arteriograms. Coronary flow reserve, evaluated by the dipyridamole/basal coronary sinus blood flow ratio, and coronary resistance reserve, evaluated by the basal/dipyridamole coronary resistance ratio, were both significantly reduced in patients with aortic regurgitation (1.67 +/- 0.40 versus 4.03 +/- 0.52 in control subjects, p less than 0.001 and 1.71 +/- 0.50 versus 4.38 +/- 0.88 in control subjects, p less than 0.001, respectively). In patients with aortic regurgitation, basal coronary sinus blood flow was higher than in control subjects (276 +/- 81 versus 105 +/- 24 ml/min, respectively, p less than 0.001) and basal coronary resistance was lower (0.31 +/- 0.13 versus 0.95 +/- 0.17 mm Hg/ml per min, respectively, p less than 0.001), but coronary blood flow and resistance after dipyridamole were not significantly different in the two groups (461 +/- 159 versus 418 +/- 98 ml/min in control subjects, 0.19 +/- 0.11 versus 0.22 +/- 0.04 mm Hg/ml per min in control subjects, respectively). These data demonstrate that coronary reserve is severely reduced in patients with chronic aortic regurgitation and exertional angina.(ABSTRACT TRUNCATED AT 250 WORDS)
Internet tomography studies the inference of the internal network performances from end-to-end measurements. Unicast probing can be advantageous for such monitoring solutions due to the wide support of unicast and the easy deployment of unicast probing paths. In this work, we propose two statistical generic methods for the inference of additive metrics using unicast probing. Our solutions give more flexibility in the choice of the collection points placement, the probed paths and they are not limited to specific topologies. Firstly, we propose the k-paths method that extends the applicability of a previously proposed solution called Flexicast for tree topologies. It is based on the Expectation-Maximization (EM) algorithm which is characterized by high computational and memory complexities. Secondly, we propose the Evolutionary Sampling Algorithm (ESA) that enhances the accuracy and the computing time but following a different approach.
Network tomography is a discipline that aims to infer the internal network characteristics from end-to-end correlated measurements performed at the network edge. This work presents a new tomography approach for link metrics inference in an SDN/NFV environment (even if it can be exported outside this field) that we called TOM (Tomography for Overlay networks Monitoring). In such an environment, we are particularly interested in supervising network slicing, a recent tool enabling to create multiple virtual networks for different applications and QoS constraints on a Telco infrastructure. The goal is to infer the underlay resources states from the measurements performed in the overlay structure. We model the inference task as a regression problem that we solve following a Neural Network approach. Since getting labeled data for the training phase can be costly, our procedure generates artificial data for the training phase. By creating a large set of random training examples, the Neural Network learns the relations between the measures done at path and link levels. This approach takes advantage of efficient Machine Learning solutions to solve a classic inference problem. Simulations with a public dataset show very promising results compared to statistical-based methods. We explored mainly additive metrics such as delays or logs of loss rates, but the approach can also be used for non-additive ones such as bandwidth.
Source routing represents a good opportunity to enhance monitoring solutions, particularly probing techniques. This technique allows deploying customized probing schemes to fulfill different monitoring needs like troubleshooting or Service Level Agreement (SLA) supervision. In this context, the use of probing cycles is a promising monitoring method. The deployment of such probing schemes becomes easier thanks to source routing since it allows constraining the traffic to follow specific paths.In this paper we propose the FEAL monitoring framework (Framework for Efficient Anomaly Localization) based on source routing probing cycles. The framework is mainly composed of two parts: the "Probing Cycles" and the "Anomaly Detection" modules. The first one defines the probing strategy by deploying the needed monitors and finding the probing cycles to cover the network topology. The "Anomaly Detection" module is based on our previously proposed statistical algorithm for the inference of link metrics named ESA (Evolutionary Sampling Algorithm) [1], here extended to more general classes of metrics. We prototype and evaluate the FEAL framework with a P4 implementation of source routing over a Mininet emulator. The results show that our framework detects and localizes efficiently the failure points in the network.
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