Nonlinear performance evaluation model for throughput of AQM scheme using full factorial design approach

Patel, Sanjeev

doi:10.1002/dac.4357

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…With the development of China's economy, the government has given a lot of support and helped to the development of enterprises. However, in order to win a certain living space in the fierce environment, enterprises need to constantly improve their competitiveness, explore and innovate, and forge ahead [1,2]. e development speed of today's society has exceeded that of any previous period.…”

Section: Introductionmentioning

confidence: 99%

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Li¹,

2022

Security and Communication Networks

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Enterprise core competence is closely related to enterprise management performance, and it is important to evaluate enterprise management performance. However, the current enterprise management performance evaluation model has the problems of high eigenvalues of sample data, low cumulative contribution and correlation, high error rate in the calculation of business management performance evaluation index weights, low evaluation accuracy, and long evaluation time. Therefore, the enterprise management performance evaluation model using improved fuzzy clustering algorithm in Internet of things (IoT) networks is proposed. First, in the IoT architecture, the enterprise management performance evaluation index system is established by using the balanced scorecard theory. Second, the evaluation index system is reduced in dimensionality by combining principal component analysis and kernel-independent component analysis, the fuzzy C-mean clustering algorithm based on the objective function is designed, and finally, the improved fuzzy clustering algorithm is obtained to establish the enterprise management performance evaluation model, the reduced evaluation index system is input, and the evaluation results are output. The results show that the sample data eigenvalue of this model is low. The maximum error rate of weight calculation is 2.3%, the accuracy is always more than 95%, and the average value of evaluation time is 0.57 s, which effectively realize enterprise management performance evaluation in IoT networks.

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Section: Introductionmentioning

confidence: 99%

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Li¹,

2022

Security and Communication Networks

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“…The performance of the proposed RLAQM algorithm was verified through simulations on NS2 simulator. The network topology for simulation is described in Figure 4, where S1~Sn are n senders, D1~Dn are n receivers, and R1~R2 are two routers [25]. The bandwidth and delay between each sender and R1, and between each receiver and R2, were set to 20 Mbps and 20 ms, respectively For comparison, RED algorithm and RLAQM algorithm were simulated under single load and variable load, and their queue lengths, throughputs, delays, and discarding probabilities were measured.…”

Section: Simulation Analysismentioning

confidence: 99%

Performance Analysis of Active Queue Management Algorithm Based on Reinforcement Learning

Jiang¹,

Feng²,

Zhu³

et al. 2020

JESA

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In the information society, data explosion has led to more congestion in the core network, dampening the network performance. Random early detection (RED) is currently the standard algorithm for active queue management (AQM) recommended by the Internet Engineering Task Force (IETF). However, RED is particularly sensitive to both service load and algorithm parameters. The algorithm cannot fully utilize the bandwidth at a low service load, and might suffer a long delay at a high service load. This paper designs the reinforcement learning AQM (RLAQM), a simple and practical variant of RED, which controls the average queue length to the predictable value under various network loads, such that the queue size is no longer sensitive to the level of congestion. Q-learning was adopted to adjust the maximum discarding probability, and derive the optimal control strategy. Simulation results indicate that RLAQM can effectively overcome the deficiency of RED and achieve better congestion control; RLAQM improves the network stability and performance in complex environment; it is very easy to migrate from RED to RLAQM on Internet routers: the only operation is to adjust the discarding probability.

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“…To assist TCP's management of network performance, the active queue management (AQM) mechanism was introduced to allow the router involved. As an effective congestion control approach, the AQM has become an attractive research topic [2]. In fact, the AQM mechanism implemented in the router has significant performance development for the network, for instance, improving network utilization, reducing packet drops, and keeping the best-effort service with low-delay [3].…”

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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Nonlinear performance evaluation model for throughput of AQM scheme using full factorial design approach

Cited by 3 publications

References 18 publications

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Performance Analysis of Active Queue Management Algorithm Based on Reinforcement Learning

An Adaptive Active Queue Management Based on Model Predictive Control

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