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

Alsaaidah, Adeeb; Zalisham, Mohd; Fadzli, Mohd; Abdel-Jaber, Hussein

doi:10.3844/jcssp.2016.289.299

Cited by 6 publications

(5 citation statements)

References 25 publications

(46 reference statements)

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“…The Markov-Modulated Bernoulli Arrival Process (MMBP) 13 used in the AQM algorithm (such as Gentle Blue, which uses a dynamic calculation of based on queue length) is based on the Bernoulli Process (BP). In this method, the traffic properties have been correlated based on discrete time.…”

Section: Related Workmentioning

confidence: 99%

Improving RED algorithm congestion control by using the Markov decision process

Mahawish

Hassan

2022

Sci Rep

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Congestion control plays an essential role on the internet to manage overload, which affects data transmission performance. The random early detection (RED) algorithm belongs to active queue management (AQM), which is used to manage internet traffic. The RED is used to eliminate weakness in default control of the Transport Control Protocol (TCP) drop-tail mechanism. The drawback of RED is parameter tuning, while adaptive RED (ARED) automatically adjusts these parameters. In this study, the suggested algorithm, the Markov decision process RED (MDPRED) uses the Markov decision process (MDP) to suitably adapt values for queue weight in the RED algorithm based on average queue length to enhance the performance of the traditional RED during TCP Slow Startup phase. This study is conducted based on fluctuations among the rate of service, queuing weight, and the mean queue length by using open-source network simulator NS3. The study shows efficient results by fluctuating end-to-end packet throughput and fast response to the inception of congestion in the network. The modified algorithm achieves a low level of drop packets by evaluating the results with other five algorithms, which is done by increasing the algorithm’s response when the average queue size becomes close to the maximum queue length threshold.

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Section: Related Workmentioning

confidence: 99%

Improving RED algorithm congestion control by using the Markov decision process

Mahawish

Hassan

2022

Sci Rep

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“…The Markov-Modulated Bernoulli Arrival Process (MMBP) [10] used in the AQM algorithm (such as Gentle Blue, which uses a dynamic calculation of max p based on queue length) is based on the Bernoulli Process (BP). In this method, the traffic properties have been correlated based on discrete time.…”

Section: Related Workmentioning

confidence: 99%

Improving RED algorithm congestion control by using the Markov Decision Process

Mahawish

Hassan

2022

Preprint

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Congestion control plays an essential role on the internet to manage overload, which affects data transmission performance. The random early detection (RED) algorithm belongs to active queue management (AQM), which is used to manage internet traffic. The RED is used to eliminate weakness in default control of the transport control protocol (TCP) drop-tail mechanism. The drawback of RED is parameter tuning, while adaptive RED (ARED) automatically adjusts these parameters. In this study, the suggested algorithm, the Markov decision process RED (MDPRED) uses the Markov decision process (MDP) to suitably adapt values for queue weight in the RED algorithm based on average queue length to enhance the performance of the traditional RED. This study is conducted based on fluctuations among the rate of service, the queuing weight, and the mean queue length by using open-source network simulator NS3. The study shows efficient results by fluctuating end-to-end packet throughput and fast response to the inception of congestion in the network. The modified algorithm achieves a low level of loss packets by evaluating the results with the default RED algorithm and ARED, which is done by increasing the algorithm’s response when the average queue size becomes close to the maximum queue length threshold.

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“…Autocorrelation is used to describe burstiness. The two-state Markov-modulate Bernoulli process (MMBP-2) model differentiates an input source into two states, then the correlation between them can be established with the Markov chain [19].…”

Section: B Related Workmentioning

confidence: 99%

Performance Analysis of Cognitive Radio Networks With Burst Dynamics

et al. 2021

IEEE Access

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The impact of the traffic characteristics of secondary users (SUs) on the performance of cognitive radio networks (CRNs) should be understood for designing operation rules. This paper focuses on multi-type burst services and network congestion problem in CRNs and evaluates the QoS of SUs based on multiple cross-layer considerations. A two-state Markov-modulate Bernoulli process (MMBP-2) is adopted to model packet flows of SUs with different burst degrees in CRNs. We propose a two-dimensional discrete queuing model to consider spectrum access, burstiness of traffic, network congestion, channel environment, user activity and finite buffer. We construct an iterative algorithm to compute the steady-state distribution of the proposed queuing model and determine the performance metrics like the throughput, the average delay, the average queue length and the total packet loss probability. Numerical analysis evaluates the QoS of SUs under different burst environments. INDEX TERMSCognitive radio networks, burst traffic, network congestion, queuing model, twodimensional Markov chain, performance evaluation.

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

Cited by 6 publications

References 25 publications

Improving RED algorithm congestion control by using the Markov decision process

Improving RED algorithm congestion control by using the Markov decision process

Improving RED algorithm congestion control by using the Markov Decision Process

Performance Analysis of Cognitive Radio Networks With Burst Dynamics

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