Enhancing TCP performance with a load‐adaptive RED mechanism

Aweya, James; Ouellette, Michel; Montuno, Delfin Y.; Chapman, A.

doi:10.1002/nem.387

Cited by 27 publications

(7 citation statements)

References 9 publications

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“…Analyzed how about active queue management along with ECN to effectively reduce packet loss over congested networks.] J. Aweya et al [12] proposed dynamically changing the threshold to enhance the effectiveness of RED under different system loads.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Queue Management Scheme for Eradicating Congestion of TFRC over Wired Environment

Reddy

Pakanati

Padmavathamma

2017

IJECE

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To accomplish increasing real time requirements, user applications have to send different kinds of data with different speeds over the internet. To effectuate the aims of the computer networks, several protocols have been added to TCP/IP protocol suite. Transport layer has to implement emerging techniques to transfer huge amount of data like multimedia streaming. To transmit multimedia applications, one of the suitable congestion control mechanisms in transport layer is TCP Friendly Rate Control Protocol (TFRC). It controls congestion based on its equation. To get more smoothed throughput, intermediate nodes (like Routers. etc.) have to use suitable procedures in all real time situations. To eradicate the level of congestion in the network, we introduce enhanced Holt-Winters equations to RED queue management algorithm and applied to TFRC. The simulation results have shown that this strategy reduces packet loss and increases throughput.

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Section: Related Workmentioning

confidence: 99%

An Enhanced Queue Management Scheme for Eradicating Congestion of TFRC over Wired Environment

Reddy

Pakanati

Padmavathamma

2017

IJECE

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“…There are many related pieces of research focused on the AQM since the first proposed AQM algorithm named Random Early Detection (RED) [4], such as adaptive RED [5], BLUE [6], and YELLOW [7]. However, these algorithms were heuristic, which made the algorithms too sensitive to parameter configuration.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Active Queue Management Based on Model Predictive Control

Ma²,

Ding

et al. 2020

IEEE Access

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In recent years, active queue management (AQM) has gained more and more attention as an important part of network congestion control. Although there are many AQM algorithms, these algorithms show weaknesses to detect and control congestion due to the complexity and dynamics of the networks. Hence, this paper proposes a new AQM algorithm based on model predictive control (MPC) theory which has been widely applied in nonlinear and time-delay systems. In order to adjust the parameters of the MPC-based AQM algorithm adaptively according to network scenario variations, the adaptive mechanism is introduced into the new algorithm, named PHAQM, by using the Hebb learning rules from the neural network control theory. The simulation results show that the algorithm is effective in avoiding network congestion. Compared to the traditional AQM schemes, such as PI, REM, and GPC algorithm, the PHAQM has a faster convergence rate and smaller queue length fluctuations and outperforms especially under dynamically changing network situations. INDEX TERMS Active queue management, adaptive control, model predictive control, network congestion control.

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“…Previous studies have shown that no fixed setting of RED parameters can meet all network conditions. [2] and [3] are two major efforts on dynamically configuring the parameters of RED gateways for different network conditions. Feng et al [Z] has found that if the average queue length avg oscillates around min,,, the RED gateway behaves too aggressively, i.e., maxv is set to too high.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], the authors have argued that it is problematic to set fixed values to the two queue thresholds, i.e., minu, and maxe:lf the thresholds are set too small, there will be many timeouts when the network,is heavily loaded; on the other hand, if the thresholds are set too large, the large amount of accumulated packets in the queue will incur large delay for each flow when the network is lightly loaded. Based on the "drop rate" as the measurement metrics of network load, they keep the actual drop rate as close to a pre-specified target drop rate as possible by adjusting the two thresholds dynamically.…”

Section: Introductionmentioning

confidence: 99%

“…If the flows conform to the committed rates, the service will be assured (i.e., in-profile); otherwise, the extra packets are marked out-ofprofle, and treated as best effort flows. For RIO, two REDparamerer sets are used: one is for in-profile packets (i.e., mi": , ma,$ , and ma,;) and the other is for out-ofprofile packets (i.e., minr' , maxph"' , and m a y Existing work on self-configuring RED gateways considers an environment with only best-effort flows (e.g., [2] and [3]). They do not discuss parameter adaptation of RED/RIO gateways for QoS networks with both assured and best effort traffic.…”

Section: Introductionmentioning

confidence: 99%

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A self-configuring RED gateway for quality of service (QoS) networks

Chien

Liao

2003

2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698)

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This paper studies self-configuring Random Early Detection (RED) gateways for Quality of Service (QoS) networks. Existing work only considers the adaptive parameter setting of RED gateways for networks with best effort flows. This paper focuses on QoS networks in which both assured and best effort flows co-exist. RED with idout (RIO) is an extension of RED for QoS networks, which maintains two parameter sets (i.e., in-profile and out-of-profile) for different classes of traffic. We find that fixed and low out-ofprofile thresholds limit best-effort throughput when assured flows are frequently absent or the amount of assured flows is small. We then propose an adaptive scheme called Adaptive-RIO to adjust the parameters according to network dynamics. From the simulation, Adaptive-RI0 performs better than RIO in terms of higher best-effort throughput and total throughput while ensuring the committed rate of assured flows.

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Enhancing TCP performance with a load‐adaptive RED mechanism

Cited by 27 publications

References 9 publications

An Enhanced Queue Management Scheme for Eradicating Congestion of TFRC over Wired Environment

An Enhanced Queue Management Scheme for Eradicating Congestion of TFRC over Wired Environment

An Adaptive Active Queue Management Based on Model Predictive Control

A self-configuring RED gateway for quality of service (QoS) networks

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