• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.22
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• pp.87-98
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• 1990

The performance analysis and capacity planning of Service Control Point which is real time response system is studied. The system is modeled by multiclass open queueing network. The analytical method is used to solve the queueing network. The solution of the model has product form solution. The focus of this paper is to investigate the capacity of system under the restriction of response time. To get the reasonable capacities, nonlinear programming problem is formulated and is solved by GINO. And the simulation model using SLAM II is formulated.

• The KIPS Transactions:PartA
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• v.9A no.1
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• pp.45-52
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• 2002

This paper proposes the fuzzy closed RCMP queueing network model using fuzzy set theory for the performance evaluation of centralized distributed processing system with ambiguous system factors in the network environments. This model can derive the measures for system performances such as the job spending time, the system throughput, average job number and server utilizations using fuzzy mean value analysis which can process the fuzzy factors. Computer simulation has been performed centralized distributed system with fuzzy service requirement time for verifying the effectiveness of derived equations of performance evaluation according to the numbers of clients, and the results were analyzed. The proposed model provides more and flexible realistic than performance evaluation of conventional method when we evaluated system performance with ambiguous factors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.153-162
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• 1997

M/G/1 queueing system is one of the most widely used one to model the real system. When operating a real systems, since it often takes cost, some control policies that change the operation scheme are adopted. In particular, the N-policy is the most popular among many control policies. Almost all researches on queueing system are based on the assumption that the arrival rates of customers into the queueing system is constant, In this paper, we consider the M/G/1 queueing system whose arrival rate varies according to the servers status : idle, set-up and busy states. For this study, we construct the steady state equations of queue lengths by means of the supplementary variable method, and derive the PGF(probability generating function) of them. The L-S-T(Laplace Stieltjes transform) of waiting time and average waiting time are also presented. We also develop an algorithm to find the optimal N-value from which the server starts his set-up. An analysis on the performance measures to minimize total operation cost of queueing system is included. We finally investigate the behavior of system operation cost as the optimal N and arrival rate change by a numerical study.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.4
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• pp.239-246
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• 2009

In this study, we analyze the mean waiting time on the nested open queueing network, where the population within each subnetwork is controlled by a semaphore queue. The queueing network can be transformed into a simpler queueing network in terms of customers waiting time. A major characteristic of this model is that the lower layer flow is halted by the state of higher layer. Since this type of queueing network does not have exact solutions for performance measure, the lower bound and upper bound on the mean waiting time are checked by comparing them with the mean waiting time in the transformed nested queueing network. Simulation estimates are obtained assuming Poisson arrivals and other phase-type arrival process, i.e., Erlang and hyper-exponential distributions. The bounds obtained can be applied to get more close approximation using the suitable approach.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.215-217
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• 2005

There are two types of packet loss probabilities used in both the network layer and the physical layer within the wireless transmitter such as a queueing discard probability and transmission loss probability. We analyze these loss performances in order to guarantee Quality of Service (QoS) which is the basic of the future network. The queuing loss probability is caused by a maximum allowable delay time and the transmission loss probability is caused by a wireless channel error. These two types of packet loss probabilities are not easily analyzed due to recursive feedback which, originates as a result at a queueing delay and a number of retransmission attempts. We consider a wireless transmitter to a M/D/1 queueing model. We configurate the model to have a finite-size FIFO buffer in order to analyze the real-time traffic streams. Then we present the approaches used for evaluating the loss probabilities of this M/D/1/K queueing model. To analyze the two types of probabilities which have mutual feedbacks with each other, we drive the solutions recursively. The validity and accuracy of the analysis are confirmed by the computer simulation. From the following solutions, we suggest a minimum of 'a Maximum Allowable Delay Time' for real-time traffic in order to initially guarantee the QoS. Finally, we analyze the required service rate for each type utilizing real-time traffic and we apply our valuable analysis to a N-user's wireless network in order to get the fundamental information (types of supportable real-type traffics, types of supportable QoS, supportable maximum number of users) for network design.

• Journal of KIBIM
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• v.8 no.3
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• pp.31-42
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• 2018

This study focuses on the BIM request for information(RFI) processing performance and quantitatively analyzes the performance of the BIM coordinator and the loss due to the waiting of the project participants. For these purposes, a method to quantitatively evaluate the performance of the BIM coordinator was proposed using a queueing model. For the verification, two projects in which BIM was applied in the construction phase were selected, and the BIM RFI data were collected through the analysis of the BIM monthly report and BIM coordinator work log of each project. In addition, the BIM input personnel, labor cost, and productivity data were collected through interviews with the experts of the case projects. The analysis of the BIM RFI processing performance of the BIM coordinator using the queueing model exhibited on a probabilistic basis that the waiting status of the project participants could vary depending on the preliminary BIM application to the design verification as well as the input number and level of the BIM coordinator personnel. In addition, the loss cost due to the waiting of the project participants was analyzed using the number of BIM RFIs waiting to be processed in the queueing system. Finally, the economic feasibility analysis for the optimal BIM coordinator input was performed considering the loss cost. The results of this study can be used to make decisions about the optimal BIM coordinator input and can provide grounds for the BIM return on investment (ROI) analysis considering the waiting cost of the project participants.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1B
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• pp.17-24
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• 2008

The effect of the variable packet size on the LRD characteristic of the MMPP traffic model is investigated. When we generate packet traffic for the performance evaluation of IP packet network, MMPP model can be used to generate packet interarrival time. And a random length of packet size from a certain distribution can be assigned to each packet. However, there is a possibility that the variable packet size might change the LRD characteristic of the original MMPP model. In this study, we investigate this possibility. For this purpose the 'refined traffic' is defined, where packet arrival time is generated according to the MMPP model and a random packet length from a specific distribution is assigned to each generated packet. Hurst parameter of the refined traffic is estimated and compared with the original Hurst parameter, which is the input parameter of the MMPP model. We also investigate the effect of the packet size distribution on the queueing performance of the MMPP traffic model and the relationship between the Hurst parameter and queueing performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.81-90
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• 2016

Different from general operating policies to be applied for controllable queueing models, two of three well-known simple N, T and D operating policies are applied alternatingly to the single server controllable queueing models, so called alternating (NT), (ND) and (TD) policies. For example, the alternating (ND) operating policy is defined as the busy period is initiated by the simple N operating policy first, then the next busy period is initiated by the simple D operating policy and repeats the same sequence after that continuously. Because of newly designed operating policies, important system characteristic such as the expected busy and idle periods, the expected busy cycle, the expected number of customers in the system and so on should be redefined. That is, the expected busy and idle periods are redefined as the sum of the corresponding expected busy periods and idle periods initiated by both one of the two simple operating policies and the remaining simple operating policy, respectively. The expected number of customers in the system is represented by the weighted or pooled average of both expected number of customers in the system when the predetermined two simple operating policies are applied in sequence repeatedly. In particular, the expected number of customers in the system could be used to derive the expected waiting time in the queue or system by applying the famous Little's formulas. Most of such system characteristics derived would play important roles to construct the total cost functions per unit time for determination of the optimal operating policies by defining appropriate cost elements to operate the desired queueing systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.66-75
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• 2017

Priority disciplines are an important scheme for service systems to differentiate their services for different classes of customers. (N, n)-preemptive priority disciplines enable system engineers to fine-tune the performances of different classes of customers arriving to the system. Due to this virtue of controllability, (N, n)-preemptive priority queueing models can be applied to various types of systems in which the service performances of different classes of customers need to be adjusted for a complex objective. In this paper, we extend the existing (N, n)-preemptive resume and (N, n)-preemptive repeat-identical priority queueing models to the (N, n)-preemptive repeat-different priority queueing model. We derive the queue-length distributions in the M/G/1 queueing model with two classes of customers, under the (N, n)-preemptive repeat-different priority discipline. In order to derive the queue-length distributions, we employ an analysis of the effective service time of a low-priority customer, a delay cycle analysis, and a joint transformation method. We then derive the first and second moments of the queue lengths of high- and low-priority customers. We also present a numerical example for the first and second moments of the queue length of high- and low-priority customers. Through doing this, we show that, under the (N, n)-preemptive repeat-different priority discipline, the first and second moments of customers with high priority are bounded by some upper bounds, regardless of the service characteristics of customers with low priority. This property may help system engineers design such service systems that guarantee the mean and variance of delay for primary users under a certain bounds, when preempted services have to be restarted with another service time resampled from the same service time distribution.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.3
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• pp.387-398
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• 1996

In this study the cell arrival processes from pre-buffer into multiplexer for MPEG(Motion Picture Experts Group) coding video sources are analyzed with consideration of the traffic periodicity in frame level. The analysis is performed by introducing the two arrival models, that is, periodic on/off source model and periodic uniform arrival model. Modulated $N^*D$/D/1 queueing system is utilized in periodic on/off source model, while ${\Sigma}{N_i}^*D_i$/D/1 queueing system is used in periodic uniform arrival model. The presented models are validated by comparing with computer simulations. Numerical results for periodic uniform arrival model are shown to be very accurate, but those of periodic on/off source model are shown to be inaccurate as the number of sources are increased.