2,753 research outputs found

    Optimizing Service Differentiation Scheme with Sized-based Queue Management in DiffServ Networks

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    In this paper we introduced Modified Sized-based Queue Management as a dropping scheme that aims to fairly prioritize and allocate more service to VoIP traffic over bulk data like FTP as the former one usually has small packet size with less impact to the network congestion. In the same time, we want to guarantee that this prioritization is fair enough for both traffic types. On the other hand we study the total link delay over the congestive link with the attempt to alleviate this congestion as much as possible at the by function of early congestion notification. Our M-SQM scheme has been evaluated with NS2 experiments to measure the packets received from both and total link-delay for different traffic. The performance evaluation results of M-SQM have been validated and graphically compared with the performance of other three legacy AQMs (RED, RIO, and PI). It is depicted that our M-SQM outperformed these AQMs in providing QoS level of service differentiation.Comment: 10 pages, 9 figures, 1 table, Submitted to Journal of Telecommunication

    VoIP Quality Assessment Technologies

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    A utility based framework for optimal network measurement

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    Packet level measurement is now routinely used to evaluate the loss and delay performance of broadband networks. In active measurement, probe packets provide samples of the loss and delay and from these samples the performance of the traffic as a whole can be deduced. However this is prone to errors: inaccuracy due to taking insufficient samples, self-interference due to injecting too many probe packets, and possible sample-correlation induced bias. In this paper we consider the optimisation of probing rate by treating all measurements as numerical experiments which can be optimally designed by using the statistical principles of design of experiments. We develop an analytical technique that quantifies an overall utility function associated with: (i) the disruption caused per probe packet, (ii) the bias and (iii) the variance as a function of the probing (sampling) rate. Our numerical results show that the optimal probing rate depends strongly on what parameter the network engineer seeks to measure.</p
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