• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.42 no.12
• /
• pp.17-24
• /
• 2005

Traditional Internet applications such as FIP and E-mail are increasingly sharing bandwidth with newer, more demanding applications such as Web browsing, IP telephony, video conference and online games. These new applications require Quality of Service (QoS), in terms of delay, loss and throughput that are different from QoS requirements of traditional applications. Unfortunately, current Active Queue Management (AQM) approaches offer monolithic best-effort service to all Internet applications regardless of the current QoS requirements. This paper proposes and evaluates a new AQM technique, called MCDT that provides dynamic and separated buffer threshold for each Applications, those are FTP and e-mail on TCP traffic, streaming services on tagged UDP traffic, and the other services on untagged UDP traffic. Using a new QoS metric, our simulations demonstrate that MCDT yields higher QoS in terms of the delay variation and a packet loss than RED when there are heavy UDP traffics that include streaming applications and data applications. MCDT fits the current best-effort Internet environment without high complexity.

• 전자공학회논문지 IE
• /
• v.43 no.3
• /
• pp.41-48
• /
• 2006

In this paper, we propose a new active queue management (AQM) scheme by utilizing the predictability of the Internet traffic. The proposed scheme predicts future traffic input rate by using the auto-regressive (AR) time series model and determines the future congestion level by comparing the predicted input rate with the service rate. If the congestion is expected, the packet drop probability is dynamically adjusted to avoid the anticipated congestion level. Unlike the previous AQM schemes which use the queue length variation as the congestion measure, the proposed scheme uses the variation of the traffic input rate as the congestion measure. By predicting the network congestion level, the proposed scheme can adapt more rapidly to the changing network condition and stabilize the average queue length and its variation even if the traffic input level varies widely. Through ns-2 simulation study in varying network environments, we compare the performance among RED, Adaptive RED (ARED), REM, Predicted AQM (PAQM) and the proposed scheme in terms of average queue length and packet drop rate, and show that the proposed scheme is more adaptive to the varying network conditions and has shorter response time.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.05a
• /
• pp.417-424
• /
• 2003

In this paper, we introduce and analyze a feedback control model of TCP/AQM dynamics. Then, we propose the Pro-active Queue Management (PAQM) mechanism, which can provide proactive congestion avoidance and control using an adaptive congestion indicator and a control function for wide range of traffic environments. The PAQM stabilizes the queue length around a desired level while giving smooth and low packet loss rates independent of the traffic load level under a wide range of traffic environment. The PAQM outperforms other AQM algorithms such as Random Early Detection (RED) [1] and PI-controller [2]

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.7
• /
• pp.923-929
• /
• 2021

TCP communication and packet communication require transmission control technology to ensure high quality and high reliability. However, in the case of real-time data transmission, an inefficient transmission problem occurs. In order to overcome this problem and transmit the packet reliability, in general, early congestion control using the buffer level as an index was used. Control of the congestion control point and the cancellation point is delayed because the point at which congestion is controlled is based on the buffer level. Therefore, in this paper, not only the buffer level indicator, but also the ideal buffer level, which determines the packet discard probability, is classified so that the transmission rate and buffer level that measure network congestion are close to the level above the optimal setting. As a result, it was shown that the average buffer level can be directly controlled by maintaining the average buffer level by the ideal buffer level set in the experiment to prove the proposed method.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.121.1-121
• /
• 2001

Congestion control is a critical issue in TCP networks, Recently, active queue management (AQM) was proposed for congestion control at routers. The random early detection RED algorithm is widely known in the AQM algorithms, We present an adaptive nonlinear RED (NRED) algorithm, which has nonlinear drop probability profile. The proposed algorithm enhanced the performance of the RED algorithm by the self-parameterization based on the traffic load Furthermore, the proposed algorithm can effectively adapt itself between he RED and the drop-tail queue management by adopting proper nonlinearity in the drop probability profile. Through simulation, we show the effectiveness of the proposed algorithm comparing with the drop-tail and the original RED algorithm.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.10e
• /
• pp.403-405
• /
• 2002

지금까지 TCP 혼잡 제어를 위한 여러 종류의 메커니즘들이 Connection을 적응성 있게 제어하기 위하여 사용되어 왔지만, TCP 혼잡 제어 메커니즘들은 성능상의 여러 문제점을 가지고 있는게 사실이다. 이에 IETF에서는 AQM(Active Queue Management) 메커니즘으로 RED 알고리즘을 권고했다. 그러나 이 또한 상이한 네트워크에서는 파래메터 설정에 따른 문제점이 있어, 네트워크 상황에 적절하게 대응하지 못하는 단점이 있다. 이러한 RED알고리즘의 문제점을 극복하고, 효율성을 개선하기 위해서 SRED, BLUE, FRED, DRED 등 다양한 AQM 메커니즘들이 제시되고 있다. 본 논문에서는 네트워크 트래픽 상황에 따라 적응성을 갖고 Threshold의 변경에 사용되는 패킷 손실율을 구하는데 있어 트래픽을 고려한가중치를 줌으로써 트래픽 상황을 반영하도록 했고, Threshold 설정에 있어 적응성 있는 단계를 통하여 큐 안정성을 개선하도록 하였다. 제안한 알고리즘의 성능 분석은 NS 시뮬레이터를 사용하였고, 제안한 Load Adaptive DRED 알고리즘과 DRED 알고리즘의 버퍼 관리 기법의 성능 비교 분석을 통하여 큐 안정성의 개선된 성능을 확인하였다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.10e
• /
• pp.181-183
• /
• 2002

현재의 인터넷 라우터는 Drop tail 방식으로 큐 안의 패킷을 관리한다. 따라서 네트워크 트래픽의 지수적인 증가로 인해 발생하는 혼잡 상황을 명시적으로 해결 한 수 없다. 이 문제를 해결하기 위해 IETF (Internet Engineering Task Force)에서는 RED(Random Early Detection)알고리즘과 같은 능동적인 큐 관리 알고리즘(AQM: Active Queue Algorithm)을 제시하였다. 하지만 RED 알고리즘은 네트워크 환경에 따른 매개 변수의 설정의 어려움을 가지고 있어 잘못된 매개변수 설정으로 인하여 네트워크 성능을 저하시키는 문제를 발생시키며 전체 망에 불안정한 혼잡제어를 야기 시킨다. 본 논문에서는 기존의 AQM를 개선한 SOQuM(Stabilized Operation of Queue Management) 알고리즘을 제안하였다. 제안한 알고리즘의 성능을 검증하기 위해 기존의 방법과 시뮬레이션을 이용하여 비교하였다.

• The Journal of the Korea Contents Association
• /
• v.10 no.3
• /
• pp.80-91
• /
• 2010

Due to the convergence of broadcasting and communications, IPTV services are spotlighted as the that next-generation multimedia services. IPTV services should have functionality such as unlimited channel capacity, extension of media, QoS awareness and are required increasing traffic and quality control technology to adapt the attributes of IPTV service. Consequently, flow based quality control techniques are needed. Therefore, many studies for providing Internet QoS are performed at IETF (Internet Engineering Task Force). As the buffer management mechanism among IP QoS methods, active queue management method such as RED(Random Early Detection) and modified RED algorithms have proposed. However, these algorithms have difficulties to satisfy the requirements of various Internet user QoS. Therefore, in this paper we propose the Flow based AQM(Active Queue Management) algorithm for the multimedia services that request various QoS requirements. The proposed algorithm can converge the packet loss ratio to the target packet loss ratio of required QoS requirements. And we present a performance evaluation by the simulations using the ns-2.

• Asian Journal of Atmospheric Environment
• /
• v.12 no.1
• /
• pp.59-66
• /
• 2018

This study aims to evaluate the performance of the Air Quality Model (AQM) for the seasonal and spatial distribution of the $NH_3$ concentration in the atmosphere. To obtain observational data for the model validation, observations based on biweekly sampling have been conducted using passive samplers since April 2015 at multiple monitoring sites in the Tokyo metropolitan area. AQM, built based on WRF/CMAQ, was applied to predict the $NH_3$ concentration observed from April 2015 to March 2016. The simulation domain includes the Kanto region, which is the most densely populated area in Japan. Because the area also contains large amount of livestock, especially in its northern part, the density of the $NH_3$ emissions derived from human activities and agriculture there are estimated to be the highest in Japan. In the model validation, the model overestimated the observed $NH_3$ concentration in the summer season and underestimated it in the winter season. In particular, the overestimation in the summer was remarkable at a rural site (Komae) in Tokyo. It was found that the overestimation at Komae was caused by the transportation of $NH_3$ emitted in the northern part of the Kanto region during the night. It is suggested that the emission input used in this study overestimated the $NH_3$ emission from human sources around the Tokyo suburbs and agricultural sources in the northern part of the Kanto region in the summer season. In addition, the current emission inventories might overestimate the difference of the agricultural $NH_3$ emissions among seasons. Because the overestimation of $NH_3$ in the summer causes an overestimation of $NO_3{^-}$ in $PM_{2.5}$ in the AQM simulation, further investigation is necessary for the seasonal variation in the $NH_3$ emissions.

• Korean Journal of Remote Sensing
• /
• v.36 no.6_3
• /
• pp.1711-1720
• /
• 2020

Particulate matter (PM) that has been artificially generated during the recent of rapid industrialization and urbanization moves and disperses according to weather conditions, and adversely affects the human skin and respiratory systems. The purpose of this study is to predict the PM10 concentration in Seoul using meteorological factors as input dataset for multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models, and compared and evaluated the performance of the models. First, the PM10 concentration data obtained at 39 air quality monitoring sites (AQMS) in Seoul were divided into training and validation dataset (8:2 ratio). The nine meteorological factors (mean, maximum, and minimum temperature, precipitation, average and maximum wind speed, wind direction, yellow dust, and relative humidity), obtained by the automatic weather system (AWS), were composed to input dataset of models. The coefficients of determination (R2) between the observed PM10 concentration and that predicted by the MLR, SVM, and RF models was 0.260, 0.772, and 0.793, respectively, and the RF model best predicted the PM10 concentration. Among the AQMS used for model validation, Gwanak-gu and Gangnam-daero AQMS are relatively close to AWS, and the SVM and RF models were highly accurate according to the model validations. The Jongno-gu AQMS is relatively far from the AWS, but since PM10 concentration for the two adjacent AQMS were used for model training, both models presented high accuracy. By contrast, Yongsan-gu AQMS was relatively far from AQMS and AWS, both models performed poorly.