• Title/Summary/Keyword: Random Early Detection (RED)

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Transmission Rate-Based Overhead Monitoring for Multimedia Streaming Optimization in Wireless Networks (무선 네트워크상에서 멀티미디어 스트리밍 최적화를 위한 전송율 기반의 오버헤드 모니터링)

  • Lee, Chong-Deuk
    • Journal of Advanced Navigation Technology
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    • v.14 no.3
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    • pp.358-366
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    • 2010
  • In the wireless network the congestion and delay occurs mainly when there are too many packets for the network to process or the sender transmits more packets than the receiver can accept. The congestion and delay is the reason of packet loss which degrades the performance of multimedia streaming. This paper proposes a novel transmission rate monitoring-based optimization mechanism to optimize packet loss and to improve QoS. The proposed scheme is based on the trade-off relationship between transmission rate monitoring and overhead monitoring. For this purpose this paper processes a source rate control-based optimization which optimizes congestion and delay. Performance evaluated RED, TFRC, and the proposed mechanism. The simulation results show that the proposed mechanism is more efficient than REC(Random Early Detection) mechanism and TFRC(TCP-friendly Rate Control) mechanism in packet loss rate, throughput rate, and average response rate.

Performance Analysis of TCP Variants using AQM and ECN (AQM과 ECN을 사용한 TCP 변종의 성능 분석)

  • Matten, Ahmad;Anwar, Adnan
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.16 no.1
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    • pp.129-137
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    • 2016
  • Transmission Control Protocol as a transport layer protocol provides steady data transfer service. There are some serious concerns about the performance of TCP over diverse networks. The vital concern in TCP network environment is congestion which may occur due to quick transmission rates or because of large number of new connections entering the network at the same time. Size of queues in routers grows thus resulting in packet drops. Retransmission of the dropped packets, and reduced throughput can prove costly. Explicit Congestion Notification (ECN) in conjunction with Active Queue Management mechanisms (AQM) such as Random early detection (RED) is used for packet marking rather than dropping. In IP packet header ECN bits can be added as a sign of congestion thus avoiding needless packet drops. The proposed ECN and AQM mechanism can be implemented with help of ns2 simulator and the performance can be tested on different TCP variants.

Achieving Relative Loss Differentiation using D-VQSDDP with Differential Drop Probability (차별적이니 드랍-확률을 갖는 동적-VQSDDP를 이용한 상대적 손실차별화의 달성)

  • Kyung-Rae Cho;Ja-Whan Koo;Jin-Wook Chung
    • Proceedings of the Korea Information Processing Society Conference
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    • 2008.11a
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    • pp.1332-1335
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    • 2008
  • In order to various service types of real time and non-real time traffic with varying requirements are transmitted over the IEEE 802.16 standard is expected to provide quality of service(QoS) researchers have explored to provide a queue management scheme with differentiated loss guarantees for the future Internet. The sides of a packet drop rate, an each class to differential drop probability on achieving a low delay and high traffic intensity. Improved a queue management scheme to be enhanced to offer a drop probability is desired necessarily. This paper considers multiple random early detection with differential drop probability which is a slightly modified version of the Multiple-RED(Random Early Detection) model, to get the performance of the best suited, we analyzes its main control parameters (maxth, minth, maxp) for achieving the proportional loss differentiation (PLD) model, and gives their setting guidance from the analytic approach. we propose Dynamic-multiple queue management scheme based on differential drop probability, called Dynamic-VQSDDP(Variable Queue State Differential Drop Probability)T, is proposed to overcome M-RED's shortcoming as well as supports static maxp parameter setting values for relative and each class proportional loss differentiation. M-RED is static according to the situation of the network traffic, Network environment is very dynamic situation. Therefore maxp parameter values needs to modify too to the constantly and dynamic. The verification of the guidance is shown with figuring out loss probability using a proposed algorithm under dynamic offered load and is also selection problem of optimal values of parameters for high traffic intensity and show that Dynamic-VQSDDP has the better performance in terms of packet drop rate. We also demonstrated using an ns-2 network simulation.

PAQM: an Adaptive and Proactive Queue Management for end-to-end TCP Congestion Control

  • Ryu Seung Wan
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2003.05a
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    • pp.417-424
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    • 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]

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Study on Detection for Cochlodinium polykrikoides Red Tide using the GOCI image and Machine Learning Technique (GOCI 영상과 기계학습 기법을 이용한 Cochlodinium polykrikoides 적조 탐지 기법 연구)

  • Unuzaya, Enkhjargal;Bak, Su-Ho;Hwang, Do-Hyun;Jeong, Min-Ji;Kim, Na-Kyeong;Yoon, Hong-Joo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.15 no.6
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    • pp.1089-1098
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    • 2020
  • In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

Performance Improvement on RED Based Gateway in TCP Communication Network

  • Prabhavat, Sumet;Varakulsiripunth, Ruttikorn
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.782-787
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    • 2004
  • Internet Engineering Task Force (IETF) has been considering the deployment of the Random Early Detection (RED) in order to avoid the increasing of packet loss rates which caused by an exponential increase in network traffic and buffer overflow. Although RED mechanism can prevent buffer overflow and hence reduce an average values of packet loss rates, but this technique is ineffective in preventing the consecutive drop in the high traffic condition. Moreover, it increases a probability and average number of consecutive dropped packet in the low traffic condition (named as "uncritical condition"). RED mechanism effects to TCP congestion control that build up the consecutive of the unnecessary transmission rate reducing; lead to low utilization on the link and consequently degrade the network performance. To overcome these problems, we have proposed a new mechanism, named as Extended Drop slope RED (ExRED) mechanism, by modifying the traditional RED. The numerical and simulation results show that our proposed mechanism reduces a drop probability in the uncritical condition.

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Design and Implementation of Efficient Adaptive RED Router Suffer Management Algorithm (Efficient Adaptive RED라우터 버퍼 관리 알고리즘 디자인과 구현)

  • Lee, Jong-Hyun;Lim, Hye-Young;Huh, Eui-Nam;Hwang, Jun;Kim, Young-Chan
    • Proceedings of the Korean Information Science Society Conference
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    • 2003.04d
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    • pp.208-210
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    • 2003
  • RED(Random Early Detection) 라우터를 성공적으로 배치하기 위해서는 RED 알고리즘을 구성하는 각 파라미터를 적절히 조절할 수 있어야 한다. 특히 다수의 TCP 연결이 하나의 라우터를 공유하는 네트워크 병목구간에서는 그 중요성이 한층 강조된다. 그러나 RED가 TD 라우터와 같은 네트워크 퍼포먼스를 유지하면서 ICP 커넥션 간 페어니스(fairness)를 향상시키기 위해서는, 네트워크 상황에 따라 다수의 컨트롤 파라미터 값을 적절하게 설정해줘야만 한다. 문제는 다양한 네트워크 환경에서 효과적으로 RED 알고리즘이 동작하기 위해 파라미터를 설정하는 것이 매우 어렵다는 것이다. 본 논문에서는 네트워크 상황에 따라 동적으로 RED 파라미터를 조절하면서 빠르게 안정적인 상태로 적응하는 진보된 RED 알고리즘인 ea-RED(Efficient Adaptive RED)를 디자인하고 구현하여 알고리즘의 효율성을 확인한다.

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A New Queue Management Algorithm for Stabilized Operation of Congestion Control (혼잡제어의 안정된 동작을 위한 새로운 큐 관리 알고리즘)

  • 구자헌;정광수;오승준
    • Proceedings of the Korean Information Science Society Conference
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    • 2002.10e
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    • pp.181-183
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    • 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.

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Internet Traffic Control Using Dynamic Neural Networks

  • Cho, Hyun-Cheol;Fadali, M. Sami;Lee, Kwon-Soon
    • Journal of Electrical Engineering and Technology
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    • v.3 no.2
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    • pp.285-291
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    • 2008
  • Active Queue Management(AQM) has been widely used for congestion avoidance in Transmission Control Protocol(TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the Back-Propagation(BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: Random Early Detection(RED) and Proportional-Integral(PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.

RIO-DC Buffer Design for Core Routers in DiffServ Assured Services

  • Hur, Kyeong
    • Journal of information and communication convergence engineering
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    • v.9 no.5
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    • pp.539-544
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    • 2011
  • In this paper, a parameter optimization method of RIO-DC (RED (Random Early Detection) with In and Out-De-Coupled Queues) scheme for Assured Service (AS) in Differentiated Services (DiffServ) is proposed. In order to optimize QoS (Quality of Service) performance of the RIO-DC policy for AS in terms of maximum tolerable latency, link utilization, fairness, etc., we should design router nodes with proper RIO-DC operating parameter values. Therefore, we propose a RIO-DC configuration method and the admission control criterion, considering the allocated bandwidth to each subclass and the corresponding buffer size, to increase throughput for In-profile traffic and link utilization. Simulation results show that RIO-DC with the proposed parameter values guarantees QoS performance comparable with the RIO scheme and it improves fairness between AS flows remarkably.