In internet communication, increasing data loads on network links can lead to router deadlock, reduced bandwidth, higher packet loss, and degraded quality of service. Traditional active queue management (AQM) methods such as Drop Tail and Random Early Detection (RED) help alleviate congestion but become ineffective when traffic spikes sharply. This paper proposes an improved RED-based algorithm, called LtRED (Lower threshold of RED), which enhances congestion control by dynamically fine-tuning the lower threshold and recalculating the average queue size, including during idle periods. LtRED adjusts the dropping behavior based on a refined estimate of the average queue length and an adaptive lower threshold to better manage both light and severe congestion. The algorithm is implemented and evaluated using the NS2 simulator, and experimental results show that LtRED outperforms standard RED in terms of packet loss rate, average queuing delay, and average throughput.
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title={eversvd,+1 (7)},
author={Unknown},
year={2026},
language={en}
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