• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1475-1482
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• 2015

In network routers, buffers are used to resolve congestion and reduce packet loss rate whenever congestion occurs at bottleneck link. Most of the existing methods to manage such buffers focus only on queue-length-based control as one loop which have some issues of low link utilization and system stability. In this paper, we propose a novel framework which exploits two-loop control method, e.g. queue-length and congestion window size, combined with optimal margin method to facilitate parameter choices. Simulation results in ns-2 demonstrate that bottleneck link performance can be improved with higher link utilization (85%) and shorter queue length (22%) than the current deployed scheme in commercial routers (RED and DropTail).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1554-1566
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• 2014

Cloud Computing allows application providers seamlessly scaling their services and enables users scaling their usage according to their needs. In this paper, using queuing game model, we present service scheduling schemes which are used in software as a service (SaaS). The object is maximizing the Cloud Computing platform's (CCP's) payoff via controlling the service requests whether to join or balk, and controlling the value of CCP's admission fee. Firstly, we treat the CCP as one virtual machine (VM) and analyze the optimal queue length with a fixed admission fee distribution. If the position number of a new service request is bigger than the optimal queue length, it balks. Otherwise, it joins in. Under this scheme, the CCP's payoff can be maximized. Secondly, we extend this achievement to the multiple VMs situation. A big difference between single VM and multiple VMs is that the latter one needs to decide which VM the service requests turn to for service. We use a corresponding algorithm solve it. Simulation results demonstrate the good performance of our schemes.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1375-1382
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• 2015

This paper aims to introduce the numerical methods for solving the optimal control theory to model bufferbloat problem. Mathematical tools are useful to provide insight for system engineers and users to understand better about what we are facing right now while experiment in a large-scale testbed can encourage us to implement in realistic scenario. In this paper, we introduce a survey of the numerical methods for solving the optimal control problem. We propose the dynamic optimization sweeping algorithm for optimal control of the active queue management. Simulation results in network simulator ns2 demonstrate that our proposed algorithm can obtain the stability faster than the others while still maintain a short queue length (≈10 packets) and low delay experience for arriving packets (0.4 seconds).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4856-4871
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• 2015

Backpressure based scheduling has been considered as a promising technique for improving the throughput of a wide range of communication networks. However, this scheduling technique has not been well studied for heterogeneous wireless networks. In this paper, we propose a virtual-queue based backpressure scheduling (VQB) algorithm for heterogeneous multi-hop wireless networks. The VQB algorithm introduces a simple virtual queue for each flow at a node for backpressure scheduling, whose length depends on the cache size of the node. When calculating flow weights and making scheduling decisions, the length of a virtual queue is used instead of the length of a real queue. We theoretically prove that VQB is throughput-optimal. Simulation results show that the VQB algorithm significantly outperforms a classical backpressure scheduling algorithm in heterogeneous multi-hop wireless networks in terms of the packet delivery ratio, packet delivery time, and average sum of the queue lengths of all nodes per timeslot.

• Journal of Korean Society of Transportation
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• v.20 no.2
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• pp.135-148
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• 2002

In this paper, a queue length calculation algorithm using image detectors has been proposed. The algorithm produces the queue length using a pair of image detectors installed both on upstream and on downstream of a corridor. In addition, a new framework for controlling the traffic signal system based on queue length has been presented. More specifically, the scheme of determining the cycle time and green split using the queue lengths has been proposed. To validate the results, a simulation study was conducted with a network environment. Results showed that the proposed method gave better operational performance than a traditional method. However, additional validation effort is necessary in order to apply the real traffic conditions.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.145-152
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• 2002

Real-time queueing information and/or predictive queue built-up information can be a good criterion in selecting travel options, such as routes, both for users, and for operators in operating transportation system. Provided properly, it will be a key information for reducing traffic congestion. Also, it helps drivers be able to select optimal roues and operators be able to manage the system effectively as a whole. To produce the predictive queue information, this paper proposes a predictive model for estimating and predicting queue lengths, mainly based on Kalman Filter. It has a structure of having state space model for predicting queue length which is set as observational variable. It has been applied for the Namsan first tunnel and the application results indicate that the model is quite reasonable in its efficacy and can be applicable for various ATIS system architecture. Some limitations and future research agenda have also been discussed.

• Journal of Communications and Networks
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• v.14 no.2
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• pp.121-129
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• 2012

In this paper, we consider a source node that operates over a time varying channel with energy harvesting capability. The goal of the source is to maximize the average number of successfully delivered packets per time slot. The source is able to choose whether to transmit a packet or defer the transmission in each time slot. The decision which is chosen by the source depends on the channel information available and the length of the energy queue. We formulate the problem of finding the optimal policy as a Markovian decision problem. We show some properties of the value function that represents the discounted number of successfully delivered packets per time slot. We prove that the optimal policy is a threshold type policy depending on the state of the channel and the length of the energy queue. We also derive an upper bound for the average number of packets per time slots successfully received by the destination. We show using numerical results that this bound is a tight bound on the performance of the optimal policy. And we consider the case of time varying channel but without channel state information (CSI). Then, we study the impact of channel time varying nature and the availability of CSI. In this case, we show that the optimal policy is a greedy policy. The performance of this greedy policy is also calculated.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.26-36
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• 2021

In this study, a deep learning model for predicting the queue length was developed using the information collected from the image detector. Then, a multiple regression analysis model, a statistical technique, was derived and compared using two indices of mean absolute error(MAE) and root mean square error(RMSE). From the results of multiple regression analysis, time, day of the week, occupancy, and bus traffic were found to be statistically significant variables. Occupancy showed the most strong impact on the queue length among the variables. For the optimal deep learning model, 4 hidden layers and 6 lookback were determined, and MAE and RMSE were 6.34 and 8.99. As a result of evaluating the two models, the MAE of the multiple regression model and the deep learning model were 13.65 and 6.44, respectively, and the RMSE were 19.10 and 9.11, respectively. The deep learning model reduced the MAE by 52.8% and the RMSE by 52.3% compared to the multiple regression model.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.396-402
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• 2001

In various applications of queueing systems, admission control is often employed. It is known that the threshold-type of admission control is optimal in many practical applications despite its simplicity. However, determining the optimal threshold value is hard in general, because analytical expressions for the stationary queue length distributions are not easily available in most queueing systems. In this paper, to quickly determine the optimal threshold value under threshold-type admission control, we develop a concurrent simulation technique, which can save large amount of CPU time required in simulation, compared to the standard simulation procedure.

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.59-88
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• 1996

This study has determined optimal type, and location of loop detector to measure accurately traffic condition influenced by traffic variation with real time. Optimal type of loop detector for through vehicle at stop bar was determined by confidences of occupancy period, and nonoccupancy period, and so appropriate detector type for application to real time traffic control system has been decided on special loop detector.