An analytical RED function design guaranteeing stable system behavior

Plasser, E.; Ziegler, Thomas

doi:10.1109/iscc.2003.1214248

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Even if evidence has demonstrated the RED algorithm performs noticeably superior to the tail drop technique, studies have disclosed that RED has several disadvantages, such as Problems of parameterization (i.e., RED parameters must be continuously adjusted to get better performance), limited throughput, large delays, and the absence of a self-adaptation strategy (Patel, 2013;Misra et al, 2000;Plasser et al, 2010;and Floyd, 2000).…”

Section: Related Workmentioning

confidence: 99%

A Priority-Based Self-Adaptive Random Early Detection Algorithm in IoT Gateways

Sada,

Olanrewaju,

Surajo

2024

jobasr

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Effective algorithms for queue management are crucial in place of guaranteeing maximum efficiency in gateway routers since network traffic continues to expand dramatically. An online researcher has suggested the Active Queue Management (AQM) strategy regarding the upcoming generation of gateway switches. The common active queue scheme remains (RED) Random Early Detection. Random early detection is susceptible to parameterization issues and lacks a self-adaptation mechanism. Several RED variants have been formed; nevertheless, variations in traffic load have an adverse effect on all of them. Due to the fact that each has a static drop pattern, to address the RED and its variation schemes, the SARED system, or the design of self-adaptive random early detection was created. But in order to prevent congestion, during the time when the queue length surpasses a present maximum threshold limit, SARED aggressively removes packets. This causes networks having a lot of traffic situations the average is expected to increase queue delay, so in those cases, SARED should be less aggressive. This paper develops a priority-based queuing congestion control method for IoT gateways to manage network congestion. Our method (priority-based algorithms) performs substantially better with regard to throughput, delay, and packet loss than the present methods of SARED. The outcomes of the conducted simulation experiments have shown that in scenarios with heavy traffic loads, priority-based self-adaptive random early detection (PSARED) has greatly decreased average queuing delay by 3%, minimized average throughput by 1%, and decreased the rate of packet loss by 10% in contrast to SARED.

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Section: Related Workmentioning

confidence: 99%

A Priority-Based Self-Adaptive Random Early Detection Algorithm in IoT Gateways

Sada,

Olanrewaju,

Surajo

2024

jobasr

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“…(2) Despite the fact that it has been demonstrated that the RED algorithm significantly outperforms the Tail Drop algorithm, research has shown that RED has some drawbacks, including parameterization issues (i.e., constant tuning of RED parameters is necessary to achieve an improved performance), low throughput, significant delays, and lack of a self-adaptation approach (Patel, S., 2013;Misra, V., et al, 2000;Plasser, E., et al, 2010 andFloyd, S., 2000). Floyd proposed Gentle RED (GRED) in (Floyd, S., 2000) to increase the throughput of RED.…”

Section: Related Workmentioning

confidence: 99%

Exponential-Self-Adaptive Random Early Detection Scheme for Queue Management in Next Generation Routers

Surajo,

Suleiman,

Yahaya

2023

FJS

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ensuring optimal performance in next-generation routers. Active Queue Management (AQM) scheme, has been advocated by the Internet research community for the next generation routers. Random Early Detection (RED) is the most well-known AQM scheme. However, RED lacks self-adaptation mechanism and it is susceptible to parametrization problem. Several variants of RED were developed, however all of them possess a static drop pattern; as such they are severely affected when a traffic load changes. To address the self-adaptation shortcoming of the RED and its variant schemes, Self-Adaptive Random Early Detection (SARED) scheme was developed. However, to avoid congestion, SARED aggressively drops packets once the queue length reached a certain maximum threshold limit, subsequently, this will increase the average queuing delay for networks with high traffic load conditions, therefore, to eliminate the aggressiveness of SARED in such situations, an Exponential version of SARED was proposed in this paper. Results of the simulation experiments carried out have indicated that in high traffic load situations, Exponential-SARED (ESARED) has significantly reduced average queuing delay by 4% and maximized average throughput by 3% compared to SARED and QERED.

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“…Current queue size q c is the most used indicator in AQM policy for estimating the probability of dropping the incoming packets. The drop probability p d can be calculated as (Plasser, Ziegler, & Reichl, 2002):…”

Section: A Effect Of Current Queue Size On Drop Probabilitymentioning

confidence: 99%

“…In Adaptive RED algorithm, p d increases linearly between the two thresholds min th and max th in dependent on the average queue size "avg." Some studies (Ohsaki & Murata, 2004;Plasser et al, 2002) showed that using linear p d function can result in forced drops when q c exceeds max th or link under-utilization when q c decreases to zero. This is an evident that the original linear drop function does not perform well within a wide range of loads.…”

Section: Drop Probability Valuesmentioning

confidence: 99%

Intelligent Reasoning Approach for Active Queue Management in Wireless Ad Hoc Networks

Natsheh

Jantan

Khatun

et al. 2007

International Journal of Business Data Communications and Networking

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Mobile ad hoc network is a network without infrastructure where every node has its own protocols and services for powerful cooperation in the network. Every node also has the ability to handle the congestion in its queues during traffic overflow. Traditionally, this was done through Drop-Tail policy where the node drops the incoming packets to its queues during overflow condition. Many studies showed that early dropping of incoming packet is an effective technique to avoid congestion and to minimize the packet latency. Such approach is known as Active Queue Management (AQM). In this article, an enhanced algorithm called fuzzy-AQM is suggested using a fuzzy logic system to achieve the benefits of AQM. Uncertainty associated with queue congestion estimation and lack of mathematical model for estimating the time to start dropping incoming packets makes the fuzzy-AQM algorithm the best choice. Extensive performance analysis via simulation showed the effectiveness of the proposed method for congestion detection and avoidance improving overall network performance.

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An analytical RED function design guaranteeing stable system behavior

Cited by 4 publications

References 11 publications

A Priority-Based Self-Adaptive Random Early Detection Algorithm in IoT Gateways

A Priority-Based Self-Adaptive Random Early Detection Algorithm in IoT Gateways

Exponential-Self-Adaptive Random Early Detection Scheme for Queue Management in Next Generation Routers

Intelligent Reasoning Approach for Active Queue Management in Wireless Ad Hoc Networks

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