Markov-modulated Bernoulli-based performance analysis for BLUE algorithm under bursty and correlated traffics

Saaidah, Adeeb; Jali, Mohd Zalisham; Marhusin, Mohd Fadzli; Abdel-Jaber, Hussein

doi:10.1109/i4ct.2014.6914136

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…In the second row of Figure 5, there are A(2) processes sampled by two elementary slots. Furthermore, the third row contains an example of sampling by 8 elementary slots, which is defined by the process A (8). In all three examples, we see the course of events typical for stationary processes (with intensities λ avg = 0.8 p/ts, 4 p/ts and 7.2 p/ts).…”

Section: Bernoulli Processmentioning

confidence: 97%

“…In Reference [7], the Markov-modulated Bernoulli process model is used to analyze the delay experienced by messages in clocked, packed-switched Banyan networks with k × k output-buffered switches. In Reference [8], when modeling bursty and correlated traffics, the authors use the two-state Markov-modulated Bernoulli arrival process. In Reference [9] four-dimensional, discrete-time Markov chain development is described and used for the performance analysis of leaky bucket algorithms.…”

Section: Related Workmentioning

confidence: 99%

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Markov-Modulated On–Off Processes in IP Traffic Modeling

2023

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This paper deals with the modeling of real IP flows using Markov-modulated On–Off processes. In the first section of the paper, we summarize the knowledge found so far about the Markov modulated On–Off regular process model, which has already been published in our previous papers. For the sake of completeness, we also summarize the well-known facts regarding the Bernoulli process. In the second section, we deal with the continuation of modeling using the Markov-modulated On–Off Bernoulli process. Our own derivation of the hitherto-unknown probability distribution of time spaces (tail distribution) is completely new. For its derivation, we used the tail distribution generating function, and then, using its derivation, we calculated the hitherto-unknown moments of the distribution (mean, variation, and third initial moment). This knowledge will allow us to create a new numerical procedure for estimating MMBP parameters from measured IP traffic. Finally, we present a formula for the sizing of network resources for a given flow using effective bandwidth with respect to QoS based on a given level of IP traffic.

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Section: Bernoulli Processmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Markov-Modulated On–Off Processes in IP Traffic Modeling

2023

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“…Step one (initialize the parameters setting): GB-MMBP-2 required settings of many parameters as seen in Table 2. GB usesa threshold value as 60% of the buffer size as those in (Saaidah et al, 2014;Abdel-Jaber et al, 2007a;Al-Diabat et al, 2012). Initially, the queue length is empty, which means the initial value of queue length is zero and the packet will be arrived initially to state 1, thus the initial state is state 1.…”

Section: Fig 2 Gb Using Mmbp-2mentioning

confidence: 99%

Markov-Modulated Bernoulli-Based Performance Analysis for Gentle BLUE and BLUE Algorithms under Bursty and Correlated Traffic

Alsaaidah¹,

Zalisham²,

Fadzli³

et al. 2016

Journal of Computer Science

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This paper presents a performance study for Gentle BLUE (GB) under the bursty and correlated properties of aggregated network traffic. The Bernoulli Process (BP) fails to represent the properties of aggregated correlated and bursty traffic, so instead of that, MMBP has been used. MMBP is A 2D discrete-time Markov chain modeling for GB algorithm with two traffic classes, each with its own parameters. The proposed model is compared with the GB that uses the BP as a source model (GB-BP) and original BLUE that uses the BP (BLUE-BP) and MMBP (BLUE-MMBP-2) as source model. The evaluation is conducted in term of queuing waiting time, mean queue length, throughput, packet loss and dropping probability. When congestion (e.g., heavy congestion) occurs, the results show that GB-MMBP-2 provides the bestmean queue length, queuing time and packet loss among the compared methods.

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“…Image registration is a fundamental operation in image analysis. It has been widely used in many applied fields, [1][2][3][4][5] including remote sensing, computer vision, medical image processing, and robotics.…”

Section: Introductionmentioning

confidence: 99%

Ensemble image registration by a spatially constrained clustering approach

Zhu

Shi

et al. 2016

International Journal of Advanced Robotic Systems

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In this article, a novel spatially constrained clustering approach is proposed for ensemble image registration. We use a spatially constrained Gaussian mixture model, which is based on a joint Gaussian mixture model and Markov random field, to model the joint intensity scatter plot of the unregistered images. The spatially constrained Gaussian mixture model has the capability of performing the correlation among neighboring observations. A cost function of reducing the dispersion in the joint intensity scatter plot is proposed using the spatially constrained Gaussian mixture model to simultaneously register a group of images. We derive an expectation maximization algorithm for the proposed model. Computer simulations demonstrate the effectiveness of the proposed method.

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Markov-modulated Bernoulli-based performance analysis for BLUE algorithm under bursty and correlated traffics

Cited by 4 publications

References 13 publications

Markov-Modulated On–Off Processes in IP Traffic Modeling

Markov-Modulated On–Off Processes in IP Traffic Modeling

Markov-Modulated Bernoulli-Based Performance Analysis for Gentle BLUE and BLUE Algorithms under Bursty and Correlated Traffic

Ensemble image registration by a spatially constrained clustering approach

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