QBLUE: A New Congestion Control Algorithm Based on Queuing Theory

Fan, Xin; Zhang, Jie; Guan, Lin; Wang, Xingang

doi:10.1109/hpcc.2012.184

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Unfortunately, the tai-drop method failed to control the congestion at the buffer. Therefore, many Active Queue Management Method (AQM) have been proposed to detect and eliminate the bursty traffic problem that exists in previous method [1,12,13]. In addition, the main goal of the AQM methods discover and manage the congestion at early before the router buffer reaches to the limit size.…”

Section: Introductionmentioning

confidence: 99%

FLACC: Fuzzy Logic Approach for Congestion Control

Baklizi¹

2019

IJACSA

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The popularity of network applications has increased the number of packets travelling within the routers in networks. The movement expends most resources in such networks and consequently leads to congestion, which worsens the performance measures of networks, such as delay, packet loss and bandwidth. This study proposes a new method called Fuzzy Logic Approach for Congestion Control (FLACC), which uses fuzzy logic to decrease delay and packet loss. This method also improves network performance. In addition, FLACC employs average queue length (aql) and packet loss (PL) as input linguistic variables to control the congestion at early stages. In this study, the proposed and compared methods were simulated and evaluated. Results reveal that fuzzy logic Gentle Random Early Detection (FLGRED) showed better performance results than Gentle Random Early Detection (GRED) and GRED Fuzzy Logic in delay and packet loss and when the router buffer was in heavy congestion.

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Section: Introductionmentioning

confidence: 99%