Estimation of video queueing performance using Markov chains

Awad, Abeer M.; McKinnon, Martin W.; Sivakumar, R.

doi:10.1109/glocom.2002.1188451

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Apart from the algorithmic approaches described above, mathematical models have also been used widely to solve and analyze related QoS problems [1,10,4,12,15,9,13,22,21]. For instance, a Markov chain modeling process is popularly used, to solve network congestion in [1], delay in [10,4,12] and packet loss in [15,9].…”

Section: Introductionmentioning

confidence: 99%

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QoS enhancements and performance analysis for delay sensitive applications

Wang

Guan

Wang

et al. 2011

Journal of Computer and System Sciences

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This paper presents a comprehensive system modeling and analysis approach for both predicting queuing delay and controlling average queuing delay of a single buffer to a required value in a multiple traffic source network environment. This approach could effectively enhance the QoS performance of delay sensitive applications. A discrete-time analytical model that approximates the multi-source arrival process with a binomial distribution has been developed to analyze the relationship between the queuing threshold and average queuing delay. A control strategy with dynamic queue thresholds based on the analytical result is then used to control the average queuing delay to a required value within the buffer. Packet dropping is treated as implicit congestion feedback to the arrival process for rate adjustment. The feasibility of the system has been validated by comparing theoretical analysis with a diverse set of simulation results. Following from the simulation results, a set of statistical analyses has been performed to evaluate the efficiency and accuracy of the proposed scheme. In addition, a user-friendly graphical user interface has been developed to allow user-configuration of the simulation process and display simulation results.

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Section: Introductionmentioning

confidence: 99%

“…For instance, a Markov chain modeling process is popularly used, to solve network congestion in [1], delay in [10,4,12] and packet loss in [15,9].…”

Section: Introductionmentioning

confidence: 99%