• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1B
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• pp.29-40
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• 2004

In this paper, we propose a new early congestion detection and notification technique called QR-AQM. Unlike RED and it's variation, QR-AQM measures the total traffic rate from TCP sessions, predicts future network congestion, and determine the packet marking probability based on the measured traffic rate. By incorporating the traffic rate in the decision process of the packet marking probability, QR-AQM is capable of foreseeing future network congestion as well as terminating congestion resolution procedure in much more timely fashion than RED. As a result, simulation results show that QR-AQM maintains the buffer level within a fairly narrow range around a target buffer level that may be selected arbitrarily as a control parameter. Consequently, compared to RED and its variations, QR-AQM is expected to significantly reduce the jitter and delay variance of packets traveling through the buffer while achieving nearly identical link utilization.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.526-533
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• 2008

We propose a wavelet neural network (WNN) control method for active queue management (AQM) in an end-to-end TCP network, which is trained by adaptive learning rates (ALRs). In the TCP network, AQM is important to regulate the queue length by passing or dropping the packets at the intermediate routers. RED, PI, and PID algorithms have been used for AQM. But these algorithms show weaknesses in the detection and control of congestion under dynamically changing network situations. In our method, the WNN controller using ALRs is designed to overcome these problems. It adaptively controls the dropping probability of the packets and is trained by gradient-descent algorithm. We apply Lyapunov theorem to verify the stability of the WNN controller using ALRs. Simulations are carried out to demonstrate the effectiveness of the proposed method.

• Journal of Satellite, Information and Communications
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• v.12 no.4
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• pp.131-140
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• 2017

Multipath TCP (MPTCP) is a transport layer protocol that simultaneously transmits data using multiple interfaces. MPTCP is superior to existing TCP in network environment with homogeneous subflows, but it shows worse performance compared to existing TCP in network environment with bufferbloat. If a bufferbloat occurs in one of the MPTCP multipaths, the packet will not arrive at the MPTCP receive buffer due to a sudden increase in delay time, resulting in a HoL blocking phenomenon. It makes the receive window of the other path to be zero. In this paper, we apply Adaptive Random Early Detection (ARED), Controlled Delay (CoDel) and Proportional Integral Controller Enhanced (PIE) among the proposed Active Queue Management (AQM) to limit the delay of bufferbloat path. Experiments were conducted to improve the performance of MPTCP in heterogeneous networks. In order to carry out the experiment, we constructed a Linux-based testbed and compared the MPTCP performance with that of the existing droptail.

• 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.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.93-105
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• 2006

The domination of the Internet by TCP-based services has spawned many efforts to provide high network utilization with low loss and delay in a simple and scalable manner. Active queue management (AQM) algorithms attempt to achieve these goals by regulating queues at bottleneck links to provide useful feedback to TCP sources. While many AQM algorithms have been proposed, most suffer from instability, require careful configuration of nonintuitive control parameters, or are not practical because of slow response to dynamic traffic changes. In this paper, we propose a new AQM algorithm, hybrid random early detection (HRED), that combines the more effective elements of recent algorithms with a random early detection (RED) core. HRED maps instantaneous queue length to a drop probability, automatically adjusting the slope and intercept of the mapping function to account for changes in traffic load and to keep queue length within the desired operating range. We demonstrate that straightforward selection of HRED parameters results in stable operation under steady load and rapid adaptation to changes in load. Simulation and implementation tests confirm this stability, and indicate that overall performances of HRED are substantially better than those of earlier AQM algorithms. Finally, HRED control parameters provide several intuitive approaches to trading between required memory, queue stability, and response time.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.767-771
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• 2004

In this paper the performance of some control theory-based Active Queue Management (AQM) methods are improved and investigated through simulation. The simulation scenario is considered to be closely realistic, in which the traffic is mainly due to http sessions. The results show the best performance of PID controller with respect to the others in good queue regulation as well as high link utilization and low delay metrics.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.159-162
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• 2005

Random Early Detection (RED), one of the most well-known Active Queue Management (AQM), has been designed to substitute Tail Drop and is nowadays widely implemented in commercially available routers. RED algorithm provides high throughput and low delay as well as a solution of global synchronization. However RED is sensitive to parameters setting, so the performance of RED, significantly depends on the fixed parameters. To solve this problem, the Adaptive RED (ARED) algorithm is suggested by S. Floyd. But, ARED also uses fixed parameters like target-queue length; it is hard to respond to bursty traffic actively. In this paper, we proposed AQM algorithm based on the variation of current queue length in order to improve adaptability about burst traffic. We measured performance of proposed algorithm through a throughput, marking-drop rate and bias phenomenon. In experimentation, we raised a packet throughput as reduced packet drop rate, and we confirmed to reduce a bias phenomenon about bursty traffic.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1807-1808
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• 2006

In this paper, we present the wavelet neural network (WNN) controller as an active queue management(AQM) in end-to-end TCP network. AQM is important to regulate the queue length and short round trip time by passing or dropping the packets at the intermediate routers. As the role of AQM, the WNN controller adaptively controls the dropping probability of the TCP network and is trained by gradient-descent algorithm. We illustrate our result that WNN controller is superior to PI controller via simulations.

• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.19-23
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• 2008

The AQM (Active Queue Management) starts dropping packets earlier to notify traffic sources about the incipient stage of congestion. The AQM improves fairness between response flow (like TCP) and non-response flow (like UDP), and it can provide high throughput and link efficiency. In this paper, we suggest the QVARED (Queue Variation Adaptive RED) algorithm to respond to bursty traffic more actively. It is possible to provide more smoothness of average queue length and the maximum packet drop probability compared to RED and ARED (Adaptive RED). Therefore, it is highly adaptable to new congestion condition. Our simulation results show that the drop rate of QVARED is decreased by 80% and 40% compare to those of RED and ARED, respectively. This results in shorter end-to-end delay by decreasing the number of retransmitted packets. Also, the QVARED reduces a bias effect over 18% than that of drop-tail method; therefore packets are transmitted stably in the bursty traffic condition.