A New Active Queue Management Algorithm: ALTDROP

Patel, Sanjeev; Gupta, Akash; Singh, Manjeet; Nikhil, Nikhil; Sharma, Vishnu

doi:10.1109/icacccn.2018.8748469

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…proposed ALTDROP [17], a random-drop adaptive queue management algorithm based on the average queue-size change rate, which can achieve better throughput in the presence of network congestion.…”

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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“…This algorithm does not manage queuing delays and may cause additional problems such as lockout, global synchronization, full queuing, and deadlock [7][8][9]. AQM controls queuing delays more effectively than Tail-Drop, and new AQM algorithms [10] have been implemented in recent years such as random early detection (RED) [11], random exponential marking (REM) [12], CHOose and Keep for responsive flows (CHOKe) [13], and the proportional integrator (PI) controller [14]. As the most representative AQM algorithm, RED [11] has been extended by many researchers [15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Effects on random early detection of the packet drop probability function with an adjustable nonlinearity

Kato

Asahara

et al. 2023

NOLTA

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This paper proposed a packet drop probability function with an adjustable nonlinearity parameter in random early detection (RED) for active queue management of a router to control network congestion. We investigated the effect of nonlinearity on the average queue size, average throughput, average retransmission rate, average round-trip time, and fairness index for two widely used loss-based congestion control algorithms: Reno and CUBIC. Simulations were performed with Tail-Drop and the original RED to clarify the effect of nonlinearity under different traffic loads. The results showed that the RED with a nonlinear function did not aggravate the network performance statistics because achieved high throughput while maintaining a low-queuing delay, such as the original RED with a linear function under the extremely heavy traffic condition. Under the light and the heavy traffic conditions, increasing the bending degree of the nonlinear function accomplished high throughput by preventing excessive discarding of packets.

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“…Although the window-based algorithm prevents buffer overflow due to discarded packets, it cannot adequately manage a queuing delay. As a result, many router-based algorithms have been proposed [2][3][4][5][6]. Random early detection (RED) [2] is one of the representative router-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fixed point analysis of a first-order discrete model for the random early detection with nonlinearity

Kato

Ito

Kousaka

2023

NOLTA

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In this paper, we propose a first-order discrete model for random early detection (RED) with nonlinearity, focusing on the average queue size in a router. The RED is a representative router-based algorithm for congestion control. Congestion is avoided by discarding packets at random using the packet drop probability function and the average queue size in RED. Our proposed model replaces the linear packet drop probability function of the original RED with a nonlinear function and can continuously change the nonlinearity strength, namely the bending degree of the nonlinear function. In other words, the model mathematically analyzes the influence of the nonlinearity strength in the packet drop probability function on the control of RED. Our simulation results showed the reproducibility of the model under three traffic conditions: light, heavy, and extremely heavy, by comparing the model and the network simulator-2. Furthermore, we performed a fixed point analysis on the steady-states to determine the average queue size. Consequently, the effect of the change in the nonlinearity strength on the average queue size was mathematically ensured.

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A New Active Queue Management Algorithm: ALTDROP

Cited by 5 publications

References 10 publications

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

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

Effects on random early detection of the packet drop probability function with an adjustable nonlinearity

Fixed point analysis of a first-order discrete model for the random early detection with nonlinearity

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