• The Journal of the Korea institute of electronic communication sciences
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• v.1 no.2
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• pp.102-109
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• 2006

In this paper, we improve the performance of bidirectional TCP connection over end-to-end network that uses transfer rate-based flow and congestion control. The sharing of a common buffer by TCP packets and acknowledgement has been known to result in an effect called ack compression, where acks of a connection arrive at the source bunched together, resulting in unfairness and degraded throughput. The degradation in throughput due to bidirectional traffic can be significant. For example, even in the simple case of symmetrical connections with adequate window size, the connection efficiency is improved about 20% for three levels of background traffic 2.5Mbps, 5.0Mbps and 7.5Mbps. Otherwise, the throughput of jitter is reduced about 50% because round trip delay time is smaller between source node and destination node. Also, we show that throughput curve is improved with connection rate algorithm which is proposed for TCP congestion avoidance as a function of aggressiveness threshold for three levels of background traffic 2.5Mbps, 5Mbps and 7.5Mbps.

• Journal of Internet Computing and Services
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• v.3 no.6
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• pp.43-51
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• 2002

In this paper, we study the new packet dropping scheme using an active queue management algorithm. Active queue management mechanisms differ from the traditional drop tail mechanism in that in a drop tail queue packets are dropped when the buffer overflows, while in active queue management mechanisms, packets may be dropped early before congestion occurs, However, it still incurs high packet loss ratio when the buffer size is not large enough, By detecting congestion and notifying only a randomly selected fraction of connection, RED causes to the global synchronization and fairness problem. And also, it is the biggest problem that the network traffic characteristics need to be known in order to find the optimum average queue length, We propose a new efficient packet dropping method based on the active queue management for congestion control. The proposed scheme uses the per-flow rate and fair share rate estimates. To this end, we present the estimation algorithm to compute the flow arrival rate and the link fair rate, We shows the proposed method improves the network performance because the traffic generated can not cause rapid fluctuations in queue lengths which result in packet loss

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.9
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• pp.1890-1906
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• 1997

A new ER feedback switch control algorithm, called EMRCA(Explicit Max-min Rate Control Algorithm), to control the ABR services traffic in the ATM networks, is proposed in this thesis. This scheme was interpreted the ACR variation residing on between a transient state and a stable state using the two thresholds that is the $TH_{high}$ and $TH_{low}$ in the queue to detect switch nodes congestion, and decreased the difference of minimum and maximum ACR all active connection to enhance the line bandwidth utilization. The proiposed scheme used a minimum and maximum rate of all active connections to select the connection whose the congestion rate inveoked, and uses a congestion detection mechanixm to prevent some potential cogestion by regulating selected contection.s The EMRCA scheme showed ACR variation more stable that the EPRCA scheme, also reduced buffer size of the switch, and achieved higher link utilization than the EPRCA scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.2
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• pp.259-267
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• 1997

The advent of B-ISDN using ATM(asynchronous transfer mode) made possible a variety of new multimedia services, however it also created a problem of congestion control due to bursty nature of various traffic sources. To tackle this problem, UPC/NPC(user parameter control/network parameter control) have been actively studied and DRLB(dynamic rate leaky bucket) algorithm, in which the token generation rate is changed according to states of data source andbuffer occupancy, is a good example of the UPC/NPC. However, the DRLB algorithm has drawbacks of low efficiency and difficult real-time implementation for bursty traffic sources because the determination of token generation rate in the algorithm is based on the present state of network. In this paper, we propose a more plastic and effective congestion control algorithm by combining the DRLB algorithm and neural network based prediction to remedy the drawbacks of the DRLB algorithm, and verify the efficacy of the proposed method by computer simulations.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.140-147
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• 1999

ABR uses the extra network resource more efficiently which uses not the guaranteed service in ATM and supports the best effort which the characteristics of application is bursty and maximum transfer rate is high. Switches for using ABR flow control differentiate EFCI switches (which supply only the congestion state information) from ER switches (which supply fair rate to source). This paper analyses the switches operation technique for efficiently managing ABR traffic in case which the network mixes EFCI switches and ER switches in ATM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1416-1425
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• 2008

Urban railway carries large numbers of passengers during the rush hour. According to the announcement of Ministry of Land Transport and Maritime Affairs(MLTM), 221% of the congestion rate is reported in the SeoulMetro No.2 outer circle line from sadang to bangbea between eight and nine o'clock in the morning. This study analyzes the cause of the client's dissatisfaction about SeoulMetro such as the train delay, and decides the preference rank of the alternatives for congestion alleviation and enhancement of the transport capacity using Analytic Hierarchy Process(AHP).

• Journal of Advanced Navigation Technology
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• v.15 no.3
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• pp.364-371
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• 2011

In P2P distribution mobile networks, QoS of streaming media services are under heavy influence from overheads such as congestion, latency, and interference. The problem is further complicated by the fact that the popularity of media objects changes over time. This paper proposes a new FSMS+ (Fuzzy Similarity-based Multi-level Streaming Scheme) which minimizes performance degradation of streaming services due to overhead. We then utilize fuzzy similarity-based relevance that can dynamically stream the streaming media object with minimum overhead. The simulation result showed that the proposed scheme has better performance in retransmission rate, congestion control rate and latency rate than the other existing methods of distance method, DC (disk caching) method, and prefix method.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.17-20
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• 2000

TCP is ill-suited to real-time multimedia applications. Its bursty transmission, and abrupt and frequent wide rate fluctuations cause high delay jitters and sudden quality degradation of multimedia applications. Deploying non congestion controlled traffic results in extreme unfairness towards competing TCP traffic. Therefore, they need to be enhanced with congestion control schemes that not only am at reducing loss ratios and improve bandwidth utilization but also are fair towards competing TCP connections. This paper proposes a differentiated rate adaptation algorithm based on loss and round trip time. Rate in a sender quickly responds to loss ratio and holds steady state. Additionally, this algorithm reduces loss ratio by loss prediction in a receiver.

• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.315-322
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• 2006

In the application rising IP multicast, the waste of available bandwidth occurs of reducing of transmission rate in non-congestion local caused by reducing of transmission rate in congestion local. In this paper, to solve such a problem, we propose a dynamic multicast method using IP multicast and overlay multicast. In proposed method, we elected a local representative for overlay multicasting. When the transmission rate of a group is less than transmission rate threshold, the representative can transmits duplicative packets. In simulation results, it was known that proposed method uses bandwidth more efficiently.