A Novel Active Queue Management Algorithm Based on Average Queue Length Change Rate

Pan, Chengsheng; Zhang, Song; Zhao, Chen; Shi, Huaifeng; Kong, Zhixiang; Cui, Xiaosong

doi:10.1109/access.2022.3189183

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…[13] proposed the delay control-based congestion control algorithm DX, which uses control theory to effectively reduce the queuing delay in the network while maintaining a high network throughput and ensuring good stability of the system. Pan C et al [14] reconfigure the packet drop policy model based on the average queue-length change rate, effectively mitigating the delay jitter problem generated by network traffic changes. Another AQM technique [15] uses a semi-Markov decision process to estimate the probability of dropping an arriving packet before it enters the buffer but requires manual setting of the target delay for various network types.…”

Section: Related Workmentioning

confidence: 99%

A Sketch-Based Fine-Grained Proportional Integral Queue Management Method

Zhu,

Sun,

Jiang

et al. 2023

Axioms

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The phenomenon “bufferbloat” occurs when the buffers of the network intermediary nodes fill up, causing long queuing delays. This has a significant negative impact on the quality of service of network applications, particularly those that are sensitive to time delay. Many active queue management (AQM) algorithms have been proposed to overcome this problem. Those AQMs attempt to maintain minimal queuing delays and good throughput by purposefully dropping packets at network intermediary nodes. However, the existing AQM algorithms mostly drop packets randomly based on a certain metric such as queue length or queuing delay, which fails to achieve fine-grained differentiation of data streams. In this paper, we propose a fine-grained sketch-based proportional integral queue management algorithm S-PIE, which uses an additional measurement structure Sketch for packet frequency share judgment based on the existing PIE algorithm for the fine-grained differentiation between data streams and adjust the drop policy for a differentiated packet drop. Experimental results on the NS-3 simulation platform show that the S-PIE algorithm achieves lower average queue length and RTT and higher fairness than PIE, RED, and CoDel algorithms while maintaining a similar throughput performance, maintaining network availability and stability, and improving network quality of service.

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Section: Related Workmentioning

confidence: 99%

A Sketch-Based Fine-Grained Proportional Integral Queue Management Method

Zhu,

Sun,

Jiang

et al. 2023

Axioms

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“…Beta Distribution Drop Functions (BetaRED) [44] extend RED using a novel queue averaging calculation. The recently proposed AC-RED (Average Queue Length Change Rate-RED) method [45] extends RED by incorporating the dynamically calculated average queue length change rate. A dynamic approach for calculating the average queue length, as proved by [46], utilizes exponential calculation and leads to improved results compared to the linear function used in RED.…”

Section: The Related Workmentioning

confidence: 99%

“…The PHAQM [19] method solely utilizes delay as an indicator in nonadaptive mechanism. On the other hand, the adaptive methods, including ARED [30], DRED [31], BLUE [32], GREEN [33], SRED [34], ATRED [39], CRED [40], AAQMRD [41], LTRED [42], WQDAQM [43], BetaRED [44] AC-RED [45], and AgRED [47] do not consider queue and traffic statuses explicitly, instead, focusing on adaptively calculating or estimating certain parameters to stabilize the performance while mitigating high packet dropping in high congested networks.…”

Section: Theoretical Comparisonmentioning

confidence: 99%

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

Alshahrani,

Abu-Shareha,

Shambour

et al. 2023

CMC

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Active queue management (AQM) methods manage the queued packets at the router buffer, prevent buffer congestion, and stabilize the network performance. The bursty nature of the traffic passing by the network routers and the slake behavior of the existing AQM methods leads to unnecessary packet dropping. This paper proposes a fully adaptive active queue management (AAQM) method to maintain stable network performance, avoid congestion and packet loss, and eliminate unnecessary packet dropping. The proposed AAQM method is based on load and queue length indicators and uses an adaptive mechanism to adjust the dropping probability based on the buffer status. The proposed AAQM method adapts to single and multiclass traffic models. Extensive simulation results over two types of traffic showed that the proposed method achieved the best results compared to the existing methods, including Random Early Detection (RED), BLUE, Effective RED (ERED), Fuzzy RED (FRED), Fuzzy Gentle RED (FGRED), and Fuzzy BLUE (FBLUE). The proposed and compared methods achieved similar results with low or moderate traffic load. However, under high traffic load, the proposed AAQM method achieved the best rate of zero loss, similar to BLUE, compared to 0.01 for RED, 0.27 for ERED, 0.04 for FRED, 0.12 for FGRED, and 0.44 for FBLUE. For throughput, the proposed AAQM method achieved the highest rate of 0.54, surpassing the BLUE method's throughput of 0.43. For delay, the proposed AAQM method achieved the second-best delay of 28.51, while the BLUE method achieved the best delay of 13.18; however, the BLUE results are insufficient because of the low throughput. Consequently, the proposed AAQM method outperformed the compared methods with its superior throughput and acceptable delay.

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“…One recent publication suggested multiple threshold levels within RED to better control the queue-length when the average queue length is between the minimum and maximum queue length allowed, after which a hard decision to queue or drop the packet can be taken [35]. As there are multiple levels of thresholds defined, calculations need more computing resources.…”

Section: Related Workmentioning

confidence: 99%

Queue-Buffer Optimization Based on Aggressive Random Early Detection in Massive NB-IoT MANET for 5G Applications

2022

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Elements in massive narrowband Internet of Things (NB-IoT) for 5G networks suffer severely from packet drops due to queue overflow. Active Queue Management (AQM) techniques help in maintaining queue length by dropping packets early, based on certain defined parameters. In this paper, we have proposed an AQM technique, called Aggressive Random Early Detection (AgRED) which, in comparison to previously used Random Early Detection (RED) and exponential RED technique, improves the overall end-to-end delay, throughput, and packet delivery ratio of the massive NB-IoT 5G network while using UDP. This improvement has been achieved due to a sigmoid function used by the AgRED technique, which aggressively and randomly drops the incoming packets preventing them from filling the queue. Because of the incorporation of the AgRED technique, the queue at different nodes will remain available throughout the operation of the network and the probability of delivering the packets will increase. We have analyzed and compared the performance of our proposed AgRED technique and have found that the performance gain for the proposed technique is higher than other techniques (RED and exponential RED) and passive queue management techniques (drop-tail and drop-head). The improvement in results is most significant in congested network deployment scenarios and provides improvements in massive Machine Type Communication, while also supporting ultra-low latency and reliable communication for 5G applications.

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A Novel Active Queue Management Algorithm Based on Average Queue Length Change Rate

Cited by 11 publications

References 25 publications

A Sketch-Based Fine-Grained Proportional Integral Queue Management Method

A Sketch-Based Fine-Grained Proportional Integral Queue Management Method

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

Queue-Buffer Optimization Based on Aggressive Random Early Detection in Massive NB-IoT MANET for 5G Applications

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