Computer Engineering Department, University of Technology-Iraq, Baghdad, Iraq.
Computer Engineering Department, Al-Iraqia University, Baghdad, Iraq.
Sci Rep. 2022 Aug 3;12(1):13363. doi: 10.1038/s41598-022-17528-x.
Congestion control plays an essential role on the internet to manage overload, which affects data transmission performance. The random early detection (RED) algorithm belongs to active queue management (AQM), which is used to manage internet traffic. The RED is used to eliminate weakness in default control of the Transport Control Protocol (TCP) drop-tail mechanism. The drawback of RED is parameter tuning, while adaptive RED (ARED) automatically adjusts these parameters. In this study, the suggested algorithm, the Markov decision process RED (MDPRED) uses the Markov decision process (MDP) to suitably adapt values for queue weight in the RED algorithm based on average queue length to enhance the performance of the traditional RED during TCP Slow Startup phase. This study is conducted based on fluctuations among the rate of service, queuing weight, and the mean queue length by using open-source network simulator NS3. The study shows efficient results by fluctuating end-to-end packet throughput and fast response to the inception of congestion in the network. The modified algorithm achieves a low level of drop packets by evaluating the results with other five algorithms, which is done by increasing the algorithm's response when the average queue size becomes close to the maximum queue length threshold.
拥塞控制在互联网中起着至关重要的作用，可用于管理过载，这会影响数据传输性能。随机早期检测 (RED) 算法属于主动队列管理 (AQM)，用于管理互联网流量。RED 用于消除传输控制协议 (TCP) 丢尾机制默认控制的弱点。RED 的缺点是参数调整，而自适应 RED (ARED) 则自动调整这些参数。在这项研究中，所提出的算法，即马尔可夫决策过程 RED (MDPRED) 使用马尔可夫决策过程 (MDP) 根据平均队列长度来适当调整 RED 算法中队列权重的值，以在 TCP 慢启动阶段增强传统 RED 的性能。本研究基于服务速率、排队权重和平均队列长度之间的波动，使用开源网络模拟器 NS3 进行。研究表明，通过在网络拥塞开始时波动端到端分组吞吐量和快速响应，可获得有效的结果。通过与其他五种算法评估结果，修改后的算法通过增加算法对平均队列大小接近最大队列长度阈值时的响应，实现了低丢包率。
