An Improved Quality-of-Service Performance Using RED’s Active Queue Management Flow Control in Classifying Networks

Alkharasani, Ameen M.; Othman, Мohamed; Abdullah, Azizol; Lun, Kweh Yeah

doi:10.1109/access.2017.2767071

Cited by 12 publications

(11 citation statements)

References 29 publications

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“…An algorithm was implemented to improve the packet drop that solved the usage savings due to the slowness of the bandwidth. As a result it was self-tuning and network traffic was managed to produce increased stability in storage parameters [23].…”

Section: Methodsmentioning

confidence: 99%

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Prototype for the Management of Engineering Companies and the ICT to Improve the Quality of Services

Toapanta¹,

Picon²,

Gallegos³

2020

Adv. sci. technol. eng. syst. j.

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Information on alternative technologies or new prototypes was analyzed to help improve the quality of business management services. The problem is the lack of application of new technologies that improve the quality of service and reduce disagreements in the management of organizations. The objective is to present a prototype for the management of engineering and ICT companies to improve the quality of services. The deductive method and exploratory research were used to analyze the information. The result was a mixed conceptual model of quality of service approach, a dual-security architecture for business management, control of algorithms with processes for quality of service and control of algorithms with information filter. It was concluded that an information filter with Blockchain is considered a fundamental alternative to provide control, reliability, immutability and improved quality of service.

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Section: Methodsmentioning

confidence: 99%

“…Modeling Equipment Hierarchy and Costs for ICT solutions [22]. An Improved Quality-of-Service Performance Using RED's Active Queue Management Flow Control in Classifying Networks [23]. Improved Quality of Service in ZigBee Network with Statistical Modeling [24].…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

Prototype for the Management of Engineering Companies and the ICT to Improve the Quality of Services

Toapanta¹,

Picon²,

Gallegos³

2020

Adv. sci. technol. eng. syst. j.

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“…Nếu kích thước hàng đợi trung bình lớn hơn maxth thì tính toán xác suất p, trao đổi giá trị giữa maxth và Uth. UTRED cho hiệu năng tốt hơn so với RED xét về tỉ lệ phân phối gói tin và thông lượng [8]. Một chiến lược khác cũng đã được đề xuất bởi Danladi và cộng sự, đó là DyRED.…”

Section: Thuật Toán Red Và Một Số Chiến Lược Quản Lý Hàng đợI độNg Khácunclassified

Nghiên Cứu, Cải Tiến Chiến Lược Hàng Đợi Red Dựa Trên Việc Tinh Chỉnh Ngưỡng Dưới

Diện¹,

Hiệp²

2022

TNUJST

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Trong những năm qua, tắc nghẽn đã trở thành vấn đề chính ảnh hưởng tới Internet, dẫn đến làm tăng tỉ lệ mất gói và làm tăng độ trễ truyền gói tin. Các thuật toán quản lý hàng đợi động (AQM-Active queue management) đã ra đời nhằm điều khiển tắc nghẽn. RED (Random Early Detection) là kỹ thuật quản lý hàng đợi động đầu tiên được triển khai để điều khiển tránh tắc nghẽn. RED dựa trên việc so sánh chiều dài trung bình hàng đợi với ngưỡng trên và ngưỡng dưới để đánh dấu hoặc loại bỏ gói tin. Mặc dù có nhiều nghiên cứu đã công bố đưa ra các thuật toán cải tiến cho RED, nhưng RED vẫn tiếp tục được quan tâm nhằm cải tiến hiệu suất được tốt hơn nữa. Trong bài báo này, nhóm tác giả đề xuất một thuật toán RED cải tiến có tên gọi là ThRED (Theshold RED) dựa trên sự tinh chỉnh ngưỡng dưới. Qua mô phỏng đánh giá trên bộ mô phỏng NS2, nhóm tác giả đã thấy được ThRED cho kết quả tốt hơn RED xét về số gói tin bị mất và độ trễ hàng đợi trung bình.

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“…The improved AQM algorithms fall into three categories: heuristic algorithms, optimization algorithms, and control algorithms [3]. Depending on intuition, the heuristic algorithm includes RED, gentle RED (GRED) [4], three-section RED (TRED) [5], fair weighted multi-level RED (FWMRED) [6], AQM with random dropping (AQMRD) [7], and new modified dropping function (NMDF) [8]. Specifically, GRED increases network stability by replacing the discontinuous change of discarding probability from Pmax to 1 should with a gentle slope.…”

Section: Introductionmentioning

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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An Improved Quality-of-Service Performance Using RED’s Active Queue Management Flow Control in Classifying Networks

Cited by 12 publications

References 29 publications

Prototype for the Management of Engineering Companies and the ICT to Improve the Quality of Services

Prototype for the Management of Engineering Companies and the ICT to Improve the Quality of Services

Nghiên Cứu, Cải Tiến Chiến Lược Hàng Đợi Red Dựa Trên Việc Tinh Chỉnh Ngưỡng Dưới

Performance Analysis of Active Queue Management Algorithm Based on Reinforcement Learning

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