Characterization Guidelines for Active Queue Management (AQM)

Natarajan, Preethi; Khademi, Navid; Kuhn, Nicolas; Ros, David

doi:10.17487/rfc7928

Cited by 12 publications

(2 citation statements)

References 9 publications

(12 reference statements)

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“…The performance of RED-QL is evaluated and contrasted against three representative AQM algorithms namely: RED, NLRED, and SmRED under light, moderate, and heavy network traffic load scenarios. These scenarios are in line with the recommendation given in RFC 7948 [5].…”

Section: Resultssupporting

confidence: 85%

“…Network congestion is considered to mean a situation in which an increase in the volume of network data traffic flows arising from growth in the active number users deploying internet-based services and applications is greater than the inflowing bandwidth capacity of the device [1]- [4]. A buffer can simply be defined as a physical volume which stores a queue or even a set of queues [5]. Active queue management (AQM) algorithm, usually implemented in routers timely manages the buffer by dropping packets before it gets saturated in order to avoid overflow and packet loss unlike the traditional Drop-Tail algorithm which drop packets only when the buffer gets saturated thereby leading to lock-out phenomenon and global synchronization problems [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

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Random early detection-quadratic linear: an enhanced active queue management algorithm

Hassan

Nwaocha²,

Rufai³

et al. 2022

Bulletin EEI

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This paper identifies the lone linear drop function for computing the dropping probability between certain queue threshold values as a major weakness for the random early detection (RED) algorithm as it leads to large delay and queue instability. To address this concern, we propose an enhanced RED-based algorithm called random early detection-quadratic linear (referred to as “RED-QL”) active queue management (AQM) which leveraged the benefit of a quadratic packet drop function for a light-to moderate traffic load conditions together with a linear packet drop function for a heavy traffic load condition respectively. Results from ns-3 network simulator using different experimental scenarios clearly reveals that the proposed RED-QL algorithm yields a substantial reduction in delay performance and indeed a reduced average queue size than other three representative AQM algorithms. RED-QL is robust, easy to implement and deploy in routers (both in software and hardware) as no more than the packet drop probability profile of the classic RED’s algorithm implementation needs modest alteration.

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Section: Resultssupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Random early detection-quadratic linear: an enhanced active queue management algorithm

Hassan

Nwaocha²,

Rufai³

et al. 2022

Bulletin EEI

View full text Add to dashboard Cite

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Intelligent Parameter Tuning Using Deep Q-Network for RED Algorithm in Adaptive Queue Management Systems

Basheer

Hassan

Muttasher

2022

Micro-Electronics and Telecommunication Engineering

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Mitigating congestion in wireless sensor networks through clustering and queue assistance: a survey

Yadav

Ujjwal

2020

J Intell Manuf

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A network of randomly deployed sensor nodes which shares limited resources like bandwidth, buffer, queue, and battery powered nodes is known as wireless sensor network. Such network must have energy, to avoid the chances of congestion because congested network degrades the performance of network. Congestion may occur due to several reasons like data packet collision, transmission channel contention and buffer overflow. A congestion control protocol must acquire the functionalities that can increase the lifetime and efficiency of network which are major responsibilities of wireless sensor network. In this paper traffic oriented, resource oriented and a hybrid approach with some additional functionalities of controlling congestion are discussed in a wide manner. The hybrid approach is best as per this survey as it integrates various factors of wireless sensor networks to control and mitigate the situation. A comprehensive analysis is also done on these factors to justify the nature of different approaches.

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Characterization Guidelines for Active Queue Management (AQM)

Abstract: Unmanaged large buffers in today's networks have given rise to a slew of performance issues. These performance issues can be

Cited by 12 publications

References 9 publications

Random early detection-quadratic linear: an enhanced active queue management algorithm

Random early detection-quadratic linear: an enhanced active queue management algorithm

Intelligent Parameter Tuning Using Deep Q-Network for RED Algorithm in Adaptive Queue Management Systems

Mitigating congestion in wireless sensor networks through clustering and queue assistance: a survey

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