Optimization of resources for H.323 endpoints and terminals over VoIP networks

Nlend, Samuel; Swart, Theo G.; Clarke, W.A.

doi:10.1109/afrcon.2011.6072160

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…which, for a small queue size, is the exponential process. A similar result using the Lamperti transform appears in [44], from which a discrete process is then interpreted as a geometrical distribution of the queue size variable 𝑛 with the same decrement factor 𝜌 𝑒𝑠 = exp(−2𝛼𝑥∕𝛽). Accordingly, for the G/G/1 queue, the geometrical distribution is written as:…”

Section: 3 Diffusion Solutionmentioning

confidence: 69%

Predictive Lossless Queue for Extremely Large Dataset Transfer in Markov Communication Systems

2022

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In this paper, a parametric prediction model is proposed for a queuing system driven by the Markov process. The aim of the model is to minimize the packet loss caused by time dependency characterized by the queue tail being too long, resulting in a time-out during the transfer of a large dataset. The proposed technique requires the key parameters influencing the queue content to be determined prior to its occupation regardless of the server capacity definition, using Bayesian inference. The subsequent time elapsing between the arrival and departure of a packet in the system is given, as well as the system load. This queue content planning is considered as the Markov birth-death chain; a type of discretization that characterizes almost all queuing systems, leading to an exponential queue, and captured herein using beta distribution. The inference results obtained using this exponential queue indicate that the queue with predictive parameters employing beta distribution, even when dealing with a loss system queue, involves less transition time and a greater load than a queue with coarse parameters; hence, preventing a long tail in the queue which is the cause of packet loss.

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Section: 3 Diffusion Solutionmentioning

confidence: 69%

Predictive Lossless Queue for Extremely Large Dataset Transfer in Markov Communication Systems

2022

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“…( 40), related to the diffusion process 𝑋, it can be deduced that the probability transition of the diffusion process is 𝑋(𝑡), by 𝑃 𝑥 (𝑡) = 𝜓 exp(−Θ𝑡) where 𝜓 is the normalization coefficient, and Θ is the diffusion coefficient of its drift and volatility with zero interference (diagonal matrix). As observed, the inference of these exponential increments can be written as in level 1, yielding the geometric distribution [29]…”

Section: Fig 3: Log-transition Probabilitymentioning

confidence: 98%

Access Convergence for Heavy Load Markov Ethernet Bursty Traffic Using Two-level Statistical Multiplexing

2022

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A method for modeling aggregated heavy Markov bursty Ethernet traffic from different sources is proposed in this paper, particularly that prevailing between gateway services and internet routing devices, with the aim of achieving rate accommodation. In other words, to accommodate different rates while filtering out delays in the queue, to achieve access network convergence. Although gateway functions can be used to achieve this by adapting service rates, as many gateways as services are required. Instead of considering the distributed gateway services method, statistical multiplexing is chosen for this study for cost efficiency in network resources. Unfortunately, statistical multiplexing exhibits greater packet variation (jitter) and transfer delay. These delays, basically resulting from positive correlations or time dependency in the queue system, are addressed through infinitesimal queue modeling, based on the diffusion process approximated by Ornstein-Uhlenbeck, which deals with infinitesimal changes in the Markov queue. The related analysis has resulted in an exponential queueing model for univariate and/or multivariate servers obtained through Markov Gaussian approximation. An experiment based on two different voice algorithms shows rate accommodation, and a fluid solution, which is dynamically outputted according to the transmission link availability during each transition time, without any significant delay. Hence, better transfer delay and rate control is obtained through the proposed two multiplexing levels within an Ethernet LAN

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Development of an effective Zapping delay framework for Internet Protocol Television over a converged network

Adeliyi¹

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Internet Protocol Television is a system that has revolutionized the media and telecommunication industries. It provides the platform for transmitting digitised television services across the Internet Protocol infrastructure. Internet Protocol Television took advantage of the Internet service convergence by providing seamless interactivity, time shifting, video on demand and pay per view to subscribers. However, zapping delay is a critical problem that deters the switching intention of terrestrial subscribers and the widespread of Internet Protocol Television services. Subscribers often experience this zapping delay problem in Internet Protocol Television when switching channels, which makes subscribers, wait for several seconds before the desired channel is found and made available. The zapping delay problem is intrinsically caused by video stream end-to-end delay, buffering delay, network jitter and traffic load. In the last few decades, a lot of frameworks, for instance, those based on multiple channels, have been proposed to reduce zapping delay in Internet Protocol Television. Such frameworks are implemented at the subscriber level, network level or video level. However, high bandwidth is still required to make the existing frameworks work effectively, which is an intrinsic limitation because not all subscribers can afford the cost of high bandwidth. This research develops a unified framework that takes the advantages provided at the subscriber level and network level to solve the zapping delay problem in Internet Protocol Television. It is possible to reduce zapping delay in Internet Protocol Television using an effective framework to aid faster channel switching and increase the quality of experience. The framework being proposed in this research is faster than a regular stream and it reduces the zapping delay to the bare minimum. The framework has been validated at both subscriber and network levels, which indicates that as traffic load increases at a set bandwidth within the converged network, packet end to end delay and network jitter should be reduced in order to eliminate zapping delay. Furthermore, the encoded and decoded video sequence available to the subscriber is evaluated using popular quantitative metrics and mean opinion score to determine subscriber perceptions of video quality through the salient object that will interest the subscriber in the video sequence displayed in order to aid a high satisfaction level video quality of experience. A large-scale implementation of the proposed framework by a telecommunication firm promises to generate revenue for the firm. In addition, the implementation and practical deployment of the proposed framework would also benefit subscribers to enjoy unlimited Internet Protocol Television services at reduced cost.

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Optimization of resources for H.323 endpoints and terminals over VoIP networks

Cited by 3 publications

References 5 publications

Predictive Lossless Queue for Extremely Large Dataset Transfer in Markov Communication Systems

Predictive Lossless Queue for Extremely Large Dataset Transfer in Markov Communication Systems

Access Convergence for Heavy Load Markov Ethernet Bursty Traffic Using Two-level Statistical Multiplexing

Development of an effective Zapping delay framework for Internet Protocol Television over a converged network

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