• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.2 s.332
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• pp.23-38
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• 2005

The state of channel between two or more wireless terminals is changed frequently due to noise or multiple environmental conditions in wireless network. In this paper, we analyze packet transmission time and queue length in a time-varying channel of packet based Wireless Networks. To reflect the feature of the time-varying channel, we model the channel as two-state Markov model and three-state Markov model Which are transformed to SFG(Signal Flow Graph) model, and then the distribution of the packet transmission can be modeled as Gaussian distribution. If the packet is arrived with Poisson distribution, then the packet transmission system is modeled as M/G/1. The average transmission time and the average queue length are analyzed in the time-varying channel, and are verified with some simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.11 no.3
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• pp.179-196
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• 1986

In this paper, the peformance of a statistical packet voice/data multiplexer is studied. In ths study we assume that in the packet voice/data multiplexer two separate finite queues are used for voice and data traffics, and that voice traffic gets priority over data. For the performance analysis we divide the output link of the multiplexer into a sequence of time slots. The voice signal is modeled as an (M+1) - state Markov process, M being the packet generation period in slots. As for the data traffic, it is modeled by a simple Poisson process. In our discrete time domain analysis, the queueing behavior of voice traffic is little affected by the data traffic since voice signal has priority over data. Therefore, we first analyze the queueing behavior of voice traffic, and then using the result, we study the queueing behavior of data traffic. For the packet voice multiplexer, both inpur state and voice buffer occupancy are formulated by a two-dimensional Markov chain. For the integrated voice/data multiplexer we use a three-dimensional Markov chain that represents the input voice state and the buffer occupancies of voice and data. With these models, the numerical results for the performance have been obtained by the Gauss-Seidel iteration method. The analytical results have been verified by computer simylation. From the results we have found that there exist tradeoffs among the number of voice users, output link capacity, voic queue size and overflow probability for the voice traffic, and also exist tradeoffs among traffic load, data queue size and oveflow probability for the data traffic. Also, there exists a tradeoff between the performance of voice and data traffics for given inpur traffics and link capacity. In addition, it has been found that the average queueing delay of data traffic is longer than the maximum buffer size, when the gain of time assignment speech interpolation(TASI) is more than two and the number of voice users is small.

• Journal of Advanced Navigation Technology
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• v.18 no.5
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• pp.461-467
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• 2014

Active queue management (AQM) algorithms run on routers and detect incipient congestion by typically monitoring the instantaneous or average queue size. When the average queue size exceeds a certain threshold, AQM algorithms infer congestion on the link and notify the end systems to back off by proactively dropping some of the packets arriving at a router or marking the packets to reduce transmission rate at the sender. Among the existing AQM algorithms, random early detection (RED) is well known as the representative queue-based management scheme by randomizing packet dropping. To reduce the number of timeouts in TCP and queuing delay, maintain high link utilization, and remove bursty traffic biases, the RED considers an average queue size as a degree of congestions. However, RED do not well in the specified networks conditions due to the fixed parameters($P_{max}$ and $TH_{min}$) of RED. This paper addresses a extended RED to be adapted in various networks conditions. By sensing network state, $P_{max}$ and $TH_{min}$ can be automatically changed to proper value and then RED do well in various networks conditions.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.23-32
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• 2017

In this paper, We suggest a unified approach for the analysis of discrete-time MAP/G/1 queueing system. Many researches on the D-MAP/G/1 queue have been used different approach to analyze system queue length and waiting time for the same system. Therefore, a unified framework for analyzing a system is necessary from a viewpoint of system design and management. We first derived steady-state workload distribution, and then waiting time and sojourn time are derived by the result of workload analysis. Finally, system queue length distribution is derived with generating function from the sojourn time distribution.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1099-1104
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• 2020

In order to provide various types of application services based on the Internet, it is ideal to guarantee the quality of service(QoS) for each flow. However, realizing these ideas is not an easy task.. It is effective to classify multiple flows having the same or similar service quality requirements into same group, and to provide service quality for each group. The queue management mechanism in the router plays a very important role in order to efficiently transmit data and to support differentiated quality of service for each service. In order to efficiently support various multimedia services, an intelligent and adaptive queue management mechanism is required. This paper proposes an intelligent queue management mechanism based on deep reinforcement learning that decides whether to deliver packets for each group based on the traffic information of each flow group flowing in for a certain period of time and the current network state information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.6
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• pp.575-596
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• 2009

Facing limited network resources such as bandwidth and processing capability, the Internet will have congestion from time to time. In this paper, we propose a scheme to maximize the total utility offered by the network to the end user during congested times. We believe the only way to achieve our goal is to make the scheme application-aware, that is, to take advantage of the characteristics of the application. To make our scheme scalable, it is designed to be class-based. Traffic from applications with similar characteristics is classified into the same class. We adopted the RED queue management mechanism to adaptively control the traffic belonging to the same class. To achieve the optimal utility, the traffic belonging to different classes should be controlled differently. By adjusting link bandwidth assignments of different classes, the scheme can achieve the goal and adapt to the changes of dynamical incoming traffic. We use the control theoretical approach to analyze our scheme. In this paper, we focus on optimizing the control on two types of traffic flows: TCP and Simple UDP (SUDP, modeling audio or video applications based on UDP). We derive the differential equations to model the dynamics of SUDP traffic flows and drive stability conditions for the system with both SUDP and TCP traffic flows. In our study, we also find analytical results on the TCP traffic stable point are not accurate, so we derived new formulas on the TCP traffic stable point. We verified the proposed scheme with extensive NS2 simulations.

• The KIPS Transactions:PartC
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• v.10C no.1
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• pp.39-44
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• 2003

The network performance gets down during congestion periods to solve the problem effectively. A RED(Random Earl Detection) algorithm of the queue management algorithm is proposed and IETF recommends it as a queue management. A RED algorithm controls a congestion aspect dynamically. In analyzing parameters when static value of parameter is set in the gateway cannot be handled the status of current network traffic properly We propose the Effective RED algorithm to solve with the weakness of RED In this algorithm the maximum drop probability decides to accept or drop the interning packets, is adjusted dynamically on the current queue state for controlling the congestion phase effectively in the gateway. This algorithm is confirmed by computer simulation using the NS(Network Simulator)-2.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.29-34
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• 2019

Priority queues, one of the fundamental data structures, have been studied for a long time by computer scientists. This paper proposes an implicit double-ended priority queue, called IMI-heap, in which insert operation takes constant amortized time and each of removal operation of the minimum key or the maximum key takes O(logn) time. To the author's knowledge, all implicit double-ended priority queues that have been published, perform insert, removeMin and removeMax operations in O(logn) time each. So, the proposed IMI-heap is superior than the published heaps in terms of insertion time complexity.The abstract should concisely state what was done, how it was done, principal results, and their significance.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.2
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• pp.42-47
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• 2005

A single-server queueing model with the Batch Markovian Arrival Process and disaster ow correlated with the arrival process is analyzed. The numerically stable algorithm for calculating the steady state distribution of the system is presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.151-157
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• 1992

This paper presents a method for analyzing the transfer lines with finite storage buffers. Each machine spends a random amount of time processing each material. This transfer line can be modeled by the tandem queueing system with finite buffers. The great dimensionality of the state space renders the analysis of such system a formidable task. We propose an efficient algorithm to obtain the marginal state probabilities based on the exact algorithm for the two-machine system. Other performance measures, such as the utilization, the blocking probability, the average sojourn time, and the average queue length, can be easily calculated.