• International Journal of Contents
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• 제13권2호
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• pp.29-34
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• 2017

Cloud computing is becoming an effective and efficient way of computing resources and computing service integration. Through centralized management of resources and services, cloud computing delivers hosted services over the internet, such that access to shared hardware, software, applications, information, and all resources is elastically provided to the consumer on-demand. The main enabling technology for cloud computing is virtualization. Virtualization software creates a temporarily simulated or extended version of computing and network resources. The objectives of virtualization are as follows: first, to fully utilize the shared resources by applying partitioning and time-sharing; second, to centralize resource management; third, to enhance cloud data center agility and provide the required scalability and elasticity for on-demand capabilities; fourth, to improve testing and running software diagnostics on different operating platforms; and fifth, to improve the portability of applications and workload migration capabilities. One of the key features of cloud computing is elasticity. It enables users to create and remove virtual computing resources dynamically according to the changing demand, but it is not easy to make a decision regarding the right amount of resources. Indeed, proper provisioning of the resources to applications is an important issue in IaaS cloud computing. Most web applications encounter large and fluctuating task requests. In predictable situations, the resources can be provisioned in advance through capacity planning techniques. But in case of unplanned and spike requests, it would be desirable to automatically scale the resources, called auto-scaling, which adjusts the resources allocated to applications based on its need at any given time. This would free the user from the burden of deciding how many resources are necessary each time. In this work, we propose an analytical and efficient VM-level scaling scheme by modeling each VM in a data center as an M/M/1 processor sharing queue. Our proposed VM-level scaling scheme is validated via a numerical experiment.

• 품질경영학회지
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• 제42권1호
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• pp.71-79
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• 2014

Purpose: The purpose of this study is to develop the methods for evaluating patients' queue environment using decision tree and queueing theory. Methods: This study uses CHAID decision tree and M/G/1 queueing theory to estimate pain point and patients waiting time for medical service. This study translates hospital physical data process to logical process to adapt queueing theory. Results: This study indicates that three nodes of the system has predictable problem with patients waiting time and can be improved by relocating patients to other nodes. Conclusion: This study finds out three seek points of the hospital through decision tree analysis and substitution nodes through the queueing theory. Revealing the hospital patients' queue environment, this study has several limitations such as lack of various case and factors.

• 대한전자공학회논문지TC
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• 제42권2호
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• pp.23-38
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• 2005

무선 네트워크에서는 여러 가지의 환경적 요인으로 인해 발생하는 페이딩 현상 및 노이즈로 인하여 무선 단말기 간의 채널의 상태가 자주 변화한다. 따라서 시변 채널 특성을 지니는 무선 네트워크에서 신뢰성 있는 데이터 전송을 위해서는 시변 채널 특성을 파악하는 것이 중요하다. 본 논문에서는 무선 네트워크 시변(Time-varying) 채널 상에서 패킷 전송 시간 및 대기큐에 대해 분석한다. 무선 네트워크 시변 채널 상태를 반영하기 위해 채널의 상태를 2-상태, 3-상태 각각의 경우로 구분하고 SFG(Signal Flow Graph) 모델을 이용하여 채널 상태를 해석한다. 각각의 상태에 대한 SFG 모델로부터 하나의 패킷에 대해 평균 전송시간과 분산을 구하고 이에 대한 확률 분포를 가우시안(Gaussian) 분포로 생각한다. 패킷의 도착분포가 프아송(Poisson) 프로세스를 따르는 전송 시스템을 M/G/1으로 모델링하고 에러 정정 기법으로 SW ARQ 기법을 적용하여 패킷의 PER의 변화 및 패킷 도착 비율의 변화에 따른 평균 패킷 전송시간과 평균 대기큐의 길이에 대해 해석하고 시뮬레이션을 통하여 검증한다.

• 한국통신학회논문지
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• 제11권3호
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• pp.179-196
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• 1986

본 논문에서는 통계적 패킷 음성/데이터 다중화기의 성능을 연구하였다. 성능해석은 음성과 데이터가 서로 분리된 한정된 queue를 사용하고, 전송에 있어서 음성이 데이터보다 우선권을 갖는 것을 가정하고, 다중화기의 출력 link를 시간 slot단위로 나누고 음성은 (M+1)-state의 Markov Process로, 데이터는 Poisson process로 modeling 하여 수행하였다. 전송시 음성신호가 데이터 신호보다 우선권을 가지므로 음성의 queueing behavior는 data에 거의 영향을 받지 않는다. 다라서 본 연구에서는 음성의 queueing behavior를 먼저 해석한 다음 data의 queueing behavior를 해석하였다. 패킷 음성 다중화기의 성능 해석은 입력상태와 buffer의 점유를 2차원의 Markov chain을 가지고 formulation하였고, 집적된 음성/data의 다중화기는 data를 추가한 3차원 Markov chain으로 하였다. 이러한 model을 사용하여 Gauss-Seidel방법으로 결과를 얻고 simulation으로 입증하였다. 이들 결과로 부터 음성 가입자의 수, 출력 link용량, 음성의 queue크기, 음성의 overflow확률에서는 서로 trade-off가 있고 data에서도 비슷한 tradeoff가 있음을 알았다. 또한 입력 traffic량과 link의 용량에 따라서 음성과 데이타간의 성능에서 서로 tradeoff가 있고, TASI의 이득이 2이상이고 음성가입자의 수가 적을 경우 데이타의 평균 지연시간은 buffer의 최대길이 보다 길음을 알아내었다.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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• pp.841-844
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• 2002

Recently there has been an increasing interest in queueing models with disasters. Upon arrival of a disaster, all the customers present are noshed out. Queueing models with disasters have been applied to the problems of failure recovery in many computer networks systems, database systems and telecommunication networks in this paper, we suffest the steady state and sojourn time distributions of the M/G/l model with disaster and mass alway when the system is empty.

• 산업경영시스템학회지
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• 제39권2호
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• pp.1-10
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• 2016

In this paper, we present a new way to derive the mean cycle time of the G/G/m failure prone queue when the loading of the system approaches to zero. The loading is the relative ratio of the arrival rate to the service rate multiplied by the number of servers. The system with low loading means the busy fraction of the system is low. The queueing system with low loading can be found in the semiconductor manufacturing process. Cluster tools in semiconductor manufacturing need a setup whenever the types of two successive lots are different. To setup a cluster tool, all wafers of preceding lot should be removed. Then, the waiting time of the next lot is zero excluding the setup time. This kind of situation can be regarded as the system with low loading. By employing absorbing Markov chain model and renewal theory, we propose a new way to derive the exact mean cycle time. In addition, using the proposed method, we present the cycle times of other types of queueing systems. For a queueing model with phase type service time distribution, we can obtain a two dimensional Markov chain model, which leads us to calculate the exact cycle time. The results also can be applied to a queueing model with batch arrivals. Our results can be employed to test the accuracy of existing or newly developed approximation methods. Furthermore, we provide intuitive interpretations to the results regarding the expected waiting time. The intuitive interpretations can be used to understand logically the characteristics of systems with low loading.

• 한국경영과학회지
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• 제19권3호
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• pp.235-241
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• 1994

We consider a B/G/1 queueing with vacations, where the server closes the gate when it begins a vacation. In this system, customers arrive according to a Bernoulli process. The service time and the vacation time follow discrete distributions. We obtain the distribution of the number of customers at a random point in time, and in turn, the distribution of the residence time (queueing time + service time) for a customer. It is observed that solutions for our discret time B/G/1 gated vacation model are analogous to those for the continuous time M/G/1 gated vacation model.

• ETRI Journal
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• 제31권1호
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• pp.86-88
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• 2009

In this letter, we consider an m-node tandem queue (queues in series) with a Poisson arrival process and either deterministic or non-overlapping service times. With the assumption that each node has a finite buffer except for the first node, we show the non-increasing convex property of stationary waiting time with respect to the finite buffer capacities. We apply it to an optimization problem which determines the smallest buffer capacities subject to probabilistic constraints on stationary waiting times.

• 산업경영시스템학회지
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• 제29권4호
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• pp.18-26
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• 2006

여러 형태의 고객이 외부로부터 포아송과정에 따라 각 대기행렬에 도착하고 정해진 서비스규칙에 따라 해당 서비스를 받은 후 마코비안 확률분포에 따라 시스템을 떠나거나 다른 형태의 고객으로 시스템을 다시 돌아 올 수도 있는 M/G/1 대기행렬시스템을 고려한다. 본 연구에서는 기존의 연구 모형을 확장하여 계층적 서비스 규칙을 갖는 우선순위 대기행렬모형을 제시하고 이에 대한 시스템 성능척도를 보다 체계적으로 구할 수 있는 방법을 소개한다. 이를 위하여 먼저 대기행렬시스템의 거동을 나타내는 시스템 상태를 정의하고, 바쁜기간과 서비스기간 분석을 통하여 시스템 상태의 선형 함수로 평균체제시간을 구할 수 있음을 보인다.

• 산업경영시스템학회지
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• 제20권43호
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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.