TRUST: A TCP Throughput Prediction Method in Mobile Networks
TRUST: A TCP Throughput Prediction Method in Mobile Networks
复制标题
DOI：
10.1109/glocom.2018.8647390
复制
发表时间：
2018-12
期刊：
影响因子：
--
通讯作者：
Bo Wei;W. Kawakami;Kenji Kanai;J. Katto;Shangguang Wang
中科院分区：
文献类型：
--
作者：
Bo Wei;W. Kawakami;Kenji Kanai;J. Katto;Shangguang Wang
吞吐量预测对于保证视频流传输的高服务质量是必不可少的。然而，当前的方法不能准确地预测移动的网络中的吞吐量，特别是对于移动用户场景。因此，我们提出了一种基于机器学习的移动的网络TCP吞吐量预测方法TRUST。TRUST分为两个阶段：用户移动模式识别和吞吐量预测。在预测阶段，采用长短期记忆（LSTM）模型预测TCP吞吐量。TRUST考虑了所有的通信质量因素、传感器数据和场景信息。进行现场实验，以评估在各种情况下的信任。实验结果表明，TRUST算法能够以更高的精度预测未来的吞吐量，在移动公交场景下，其吞吐量预测误差最大可降低44%。
Throughput prediction is essential for ensuring high quality of service for video streaming transmissions. However, current methods are incapable of accurately predicting throughput in mobile networks, especially for moving user scenarios. Therefore, we propose a TCP throughput prediction method TRUST using machine learning for mobile networks. TRUST has two stages: user movement pattern identification and throughput prediction. In the prediction stage, the long short-term memory (LSTM) model is employed for TCP throughput prediction. TRUST takes all the communication quality factors, sensor data and scenario information into consideration. Field experiments are conducted to evaluate TRUST in various scenarios. The results indicate that TRUST can predict future throughput with higher accuracy than the conventional methods, which decreases the throughput prediction error by maximum 44% under the moving bus scenario.