Abstract:

Precisely predicting the trend of network traffic changes can help operators accurately predict network usage, correctly allocate and efficiently use network resources to meet the growing and diverse user needs. Taking the progress of deep learning algorithms in the field of network traffic prediction as a clue, this paper firstly elaborates the evaluation indicators of network traffic prediction and the current public network traffic data sets. Secondly, this paper specifically analyzes four deep learning methods commonly used in network traffic prediction：deep belief networks, convolutional neural network, recurrent neural network, and long short term memory network, and focuses on the integrated neural network models used in recent years for different problems. The characteristics and application scenarios of each model are summarized. Finally, the future development of network traffic forecast is prospected.

Key words: deep learning, network traffic prediction, deep belief networks, convolutional neural network, long short term memory network

摘要：

精准地预判网络流量变化趋势可以帮助运营商准确预估网络的使用情况，合理分配并高效利用网络资源，以满足日益增长且多样化的用户需求。以深度学习算法在网络流量预测领域的进展为线索，阐述了网络流量预测的评价指标和目前公开的网络流量数据集及应用，具体分析了网络流量预测中常用的深度信念网络、卷积神经网络、循环神经网络和长短时记忆网络共四种深度学习方法，并重点介绍了近年来针对不同问题所提出的改进神经网络模型，总结了各模型特点及应用场景。最后对网络流量预测未来发展进行了展望。

关键词: 深度学习, 网络流量预测, 深度信念网络, 卷积神经网络, 长短时记忆网络