• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.383-386
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• 2001

This paper presents a novel multicast architecture for shared multibuffer ATM switch, which is tailored for throughput enhancement in multicast environments. The address queues for multicast cells are separated from those for unicast cells to arbitrate multicast cells independently from unicast cells. Three read cycles are carried out during each cell slot and multicast cells have chances to be read from shared buffer memory(SBM) in the third read cycle provided that the shared memory is not accessed to read a unicast cell. In this architecture, maximum two cells are queued at each fabric output port per time slot and output mask choose only one cell. Extensive simulations are carried out and it shows that the proposed architecture has enhanced throughput comparing with other multicast schemes in shared multibuffer switch architecture.

• Journal of applied mathematics & informatics
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• v.6 no.1
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• pp.309-320
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• 1999

Models of single-server queues with vacations have been widely used to study the performance of many computer communi-cation and production systems. In this paper we analyze an M/G/1 queue with generalized vacations and exhaustive service. This sys-tem has been shown to possess a stochastic decomposition property. That is the customer waiting time in this system is distributed as the sum of the waiting time in a regular M/G/1 queue with no va-cations and the additional delay due to vacations. Herein a general formula for the additional delay is derived for a wide class of vacation policies. The formula is also extended to cases with multiple types of vacations. Using these new formulas existing results for certain vacation models are easily re-derived and unified.

• Journal of applied mathematics & informatics
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• v.38 no.3_4
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• pp.351-373
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• 2020

There is an extensive literature on retrial queueing models. While a majority of the literature on retrial queueing models focuses on the retrial times to be exponentially distributed (so as to keep the state space to be of a reasonable size), a few papers deal with nonexponential retrial times but with some additional restrictions such as constant retrial rate, only the customer at the head of the retrial queue will attempt to capture a free server, 2-state phase type distribution, and finite retrial orbit. Generally, the retrial queueing models are analyzed as level-dependent queues and hence one has to use some type of a truncation method in performing the analysis of the model. In this paper we study a retrial queueing model with threshold-type policy for orbiting customers in the context of nonexponential retrial times. Using matrix-analytic methods we analyze the model and compare with the classical retrial queueing model through a few illustrative numerical examples. We also compare numerically our threshold retrial queueing model with a previously published retrial queueing model that uses a truncation method.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.69-78
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• 2012

Reduction of power consumption has been a major issue and an interesting challenge to maximize the lifetime of wireless sensor networks. We investigate the practical meaning of N-policy in queues as a power saving technique in a WSN. We consider the N-policy of a finite M/M/1 queue. We formulate the optimization problem of power consumption considering the packet loss probability. We analyze the trade-off between power consumption and the packet loss probability and demonstrate the operational characteristics of N-policy as a power saving technique in a WSN with various numerical examples.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.4
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• pp.329-333
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• 2003

In this paper we analyze the performance of the low latency pre-registration handoff method of mobile IP by computer simulation. Packet losses and delays are evaluated in terms of system utilization. Foreign agents that participate in the handoff process can be modeled as queues representing input and output ports. Therefore, we propose an analytical model of pre-registration handoff by using open queueing network model. Simulation results are shown for validating analytical estimates.

• Management Science and Financial Engineering
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• v.9 no.2
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• pp.1-12
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• 2003

For a stationary queue with BMAP arrivals, Takine and Takahashi [8] present a relationship between the queue length distributions at a random epoch and at a departure epoch by using the rate conservation law of Miyazawa [6]. In this note, we derive the same relationship by using the elementary balance equation, ‘rate-in = rate-out’. Along the same lines, we additionally derive relationships between the queue length distributions at a random epoch and at an arrival epoch. All these relationships hold for a broad class of finite-as well as infinite-capacity queues with BMAP arrivals.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.51-61
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• 2002

By using the arrival time approach of Chae et at. [6], we derive various performance measures including the queue length distributions (in PGFs) and the waiting time distributions (in LST and PGF) for both M$^{x}$ /G/1 and Geo$^{x}$ /G/1 queueing systems, both under the assumption that the server, when it becomes idle, takes multiple vacations up to a random maximum number. This is an extension of both Choudhury[7] and Zhang and Tian [11]. A few mistakes in Zhang and Tian are corrected and meaningful interpretations are supplemented.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.4
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• pp.443-457
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• 2015

We consider the M/M/c/c queues in which the customers blocked to enter the service facility retry after a random amount of time and some of idle servers can leave the vacation. The vacation time and retrial time are assumed to be of phase type distribution. Approximation formulae for the distribution of the number of customers in service facility and the mean number of customers in orbit are presented. We provide an approximation for M/M/c/c queue with general retrial time and general vacation time by approximating the general distribution with phase type distribution. Some numerical results are presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.6
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• pp.1470-1479
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• 1996

It was already proved that ATM has stability and flexibility of service applications, but because of the various characteristics of traffic and the QoS on user demandcaused by them, there are so many researches for traffic control method. In this paper, in order to give the more similar properties of the real traffic to the existing input traffic source, dividing them into four classes with different characteristics, and We proposed the integrated dynamic priority method, IDP which has advantages of the DMLT and the DQLT for time dynamic priority. Thus, it makes input queues about each of traffic classes and thresholds, and then use modified DMLT and DQLT methods. It has goal that proposed method to get more reliability and stability with advantage low loss of the DMLT, low delay of DQLT.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.6 no.8
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• pp.93-103
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• 1983

In this paper, we study an optimal service policy of the M/M/2/K queueing system with two types of customers. The incurred costs consist of waiting cost, service cost and incurred costs consist of waiting cost, service cut and changeover cost. The changeover cost occurs when a server who assigned to serve a particular type of customers reassigned to the other types of customers. Two servers serve two types of customers who arrive to the two separate queues. The two types of customers differ in respect of their arrival rate, service rate, waiting cost, and service cost. The servers require a policy, for determining when they should change their service type, which minimizes the long run expected total cost. The policy is obtained by a Markov decision process model that consists of a finite number of states and actions. In order to find the optimal service policy, we define states and actions of the system, compute onestep transition probabilities, and apply to the successive approximations algorithm.