Analysis and estimation of overflow probability in Jackson queueing networks

Panda, Rama Ranjan; Reza, Motahar

doi:10.1109/c2spca.2013.6749397

Cited by 2 publications

(3 citation statements)

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“…The different methods that are associated for the estimation of the buffer overflow in queueing networks are discussed [17].Using Mamdani fuzzy inference system the overflow probabilities is optimized to obtain better quality of service (QoS).…”

Section: Fig 1a Telecommunication Networkmentioning

confidence: 99%

“…Finding Quality of Service (QoS) any cast path from client (source) to any of the server in a Mobile ad hoc Networks (MANET) by using software agents is discussed [16]. A single MlMII queue and a two MlMIl queue in tandem Jackson network is considered for analyzing and estimating the buffer overflow probability In telecommunication networks [17].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of buffer overflow probability in Jackson queueing networks using Mamdani fuzzy inferecnce system

Panda

Reza

2014

2014 2nd International Conference on Business and Information Management (ICBIM)

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In tbis paper we consider Mamdani fuzzy inference system to study for optimizing tbe buffer overflow probability in a single MIMIl queue and two MIM/l queue in tandem Jackson networks. Mamdani fuzzy inference system is proposed to estimate tbe service rate for tbe Jackson queueing network. Tbe arrival rate and tbe bigbest overflow level of a particular queue are provided to tbe Mamdani fuzzy inference system wbicb generates tbe service rate according to tbe fuzzy rule base.Tbe arrival rate, tbe bigbest overflow level and tbe new service rate will be used to estimate tbe buffer overflow in a single MlMIlqueueing network tben in two queues in tandem queueing network. Simulation results sbows tbat Mamdani fuzzy inference system reduce tbe buffer overflow probability as compared witb tbe normal estimation of buffer overflow probability.Keywords-Jackson Network; overflow probability; Mamdani fuzzy inference system; Rule base I.

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Section: Fig 1a Telecommunication Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of buffer overflow probability in Jackson queueing networks using Mamdani fuzzy inferecnce system

Panda

Reza

2014

2014 2nd International Conference on Business and Information Management (ICBIM)

Self Cite

View full text Add to dashboard Cite

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“…Queueing networks are an effective technique for studying the efficiency of ride-sharing or vehicular applications [7][8][9], transportation networks [10], communication networks [5,11], and so on. For more details, one may refer to the distinguished works of Kulkarni [12], Rama and Motahar [13], Paschalidis and Tsitsiklis [14], Sommer et al [15], Cruz and Woensel [16], Roy [17], Kim et al [18], Melamed [19], Kam et al [20], Kaul et al [21], Kam et al [22], Whitt and You [23], Whitt [24], Xia and Shihada [25], Timmer and Scheinhardt [26], Wang et al [27], and Alam et al [28], etc.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Data Distributed Network System under Uncertainty

Sahoo

Sen

Tiwary

et al. 2022

Discrete Dynamics in Nature and Society

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The major network design or data distributed problems may be described as constrained optimization problems. Constrained optimization problems include restrictions imposed by the system designers. These limitations are basically due to the system design’s physical limitations or functional requirements of the network system. Constrained optimization is a computationally challenging job whenever the constraints/limitations are nonlinear and nonconvex. Furthermore, nonlinear programming methods can easily deal same optimization problem if somehow the constraints are nonlinear and convex. In this paper, we have addressed a distributed network design problem involving uncertainty that transmits data across a parallel router. This distributed network design problem is a Jackson open-type network design problem that has been formulated based on the M/M/1 queueing system. Because our network design problem is a nonlinear, convex optimization problem, we have employed a well-known Kuhn–Tucker (K-T) optimality algorithm to solve the same. Here, we have used triangular fuzzy numbers to express uncertain traffic rates and data processing rates. Then, by applying α -level interval of fuzzy numbers and their corresponding parametric representation of α -level intervals, the associated network design problem has been transformed to its parametric form and later has been solved. To obtain the optimal data stream rate in terms of interval and to illustrate the applicability of the entire approach, a hypothetical numerical example has been exhibited. Finally, the most important results have been reported.

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Analysis and estimation of overflow probability in Jackson queueing networks

Cited by 2 publications

References 16 publications

Optimization of buffer overflow probability in Jackson queueing networks using Mamdani fuzzy inferecnce system

Optimization of buffer overflow probability in Jackson queueing networks using Mamdani fuzzy inferecnce system

Optimization of Data Distributed Network System under Uncertainty

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