4,926 research outputs found

    A quantitative analysis and performance study of fast congestion notification (FN) mechanism

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    Congestion in computer network happens when the number of transmission requests exceeds the transmission capacity at a certain network point (called a bottle-neck resource) at a specific time. Congestion usually causes buffers overflow and packets loss. The purpose of congestion management is to maintain a balance between the transmission requests and the transmission capacity so that the bottle-neck resources operate on an optimal level, and the sources are offered service in a way that assures fairness. Fast Congestion Notification (FN) is one of the proactive queue management mechanisms that limits the queuing delay and achieves the maximum link utilization possible with minimum packet drops. In this paper we present a detailed performance comparison of the Linear FN algorithm to RED based on the results obtained through simulations. The paper shows how FN can be tuned for different window size (Ws) and periods of time constant (T) to achieve higher link utilization; reduce the queuing delay, and lower packet drop ratio

    Prototyping Design and Optimization of Smart Electric Vehicles/Stations System using ANN

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    This paper demonstrates an experimental attempt to prototype electric vehicle charging station’s (EVCS) decision-making unit, using artificial neural network (ANN) algorithm. The algorithm acts to minimize the queuing delay in the station, with respect to the vehicle state of charge (SOC), and the expected arrival time. A simplified circuit model has been used to prototype the proposed algorithm, to minimize the overall queuing delay. Herein, the worst-case scenario is considered by having number of electric vehicles arriving to the station at the same time greater than the charging points available in the station side. Accordingly, the optimization technique was applied to reduce the mean charging time of the vehicles and minimize queuing delay. Results showed that this model can help in reducing the queuing delay by around 20% of the mean charging time of the station, while referring to a bare model without ANN algorithm as a reference

    Measuring the dynamical state of the Internet: Large-scale network tomography via the ETOMIC infrastructure

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    In this paper we show how to go beyond the study of the topological properties of the Internet, by measuring its dynamical state using special active probing techniques and the methods of network tomography. We demonstrate this approach by measuring the key state parameters of Internet paths, the characteristics of queuing delay, in a part of the European Internet. In the paper we describe in detail the ETOMIC measurement platform that was used to conduct the experiments, and the applied method of queuing delay tomography. The main results of the paper are maps showing various spatial structure in the characteristics of queuing delay corresponding to the resolved part of the European Internet. These maps reveal that the average queuing delay of network segments spans more than two orders of magnitude, and that the distribution of this quantity is very well fitted by the log-normal distribution. Copyright © 2006 S. Karger AG

    Congestion window control based on queuing delay and packet loss

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    A method of controlling size of a congestion window, includes, at a transmitting device, transmitting a plurality of data packets over a communication channel from the transmitting device to a receiver, determining a queuing delay and a loss rate of the transmission, comparing the queuing delay to a threshold queuing delay, comparing the loss rate to a threshold loss rate, and in response to a determination that the queuing delay is greater than the threshold queuing delay and the loss rate is greater than the threshold loss rate, resetting the size of the congestion window in accordance with a function of the current size of the congestion window, the queuing delay, and the loss rate, wherein at equilibrium the function generates a value inversely proportional to a weighted sum of an excess queuing delay and an excess loss rate