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LNTP: An End-to-End Online Prediction Model for Network Traffic
IEEE NETWORK ( IF 6.8 ) Pub Date : 12-14-2020 , DOI: 10.1109/mnet.011.1900647
Lianming Zhang 1 , Huan Zhang 1 , Qian Tang 1 , Pingping Dong 1 , Zhen Zhao 1 , Yehua Wei 1 , Jing Mei 1 , Kaiping Xue 2
Affiliation  

As network data keeps getting bigger, deep learning is coming to play a key role in network design and management. Meanwhile, accurate network traffic prediction is of critical importance for network management that is implemented to improve the quality of service (QoS) for users. However, the performance of existing network traffic prediction methods is still poor due to three challenges: complicated characteristics of network traffic, dynamics of traffic patterns caused by different network applications, and a complex set of variations like burstiness. In this article, we propose a long short-term memory (LSTM) based network traffic prediction (LNTP) model, which aims to forecast network traffic timely and accurately. The model can be divided into two parts, namely, wavelet transform and LSTM. The working process of LNTP falls into three stages, i.e., data acquisition, model training, and online learning and prediction. In addition, to avoid the negative incentives to models caused by the burstiness and adapt to the changing trend of the network traffic, a weight optimization algorithm of the neural network named sliding window gradient descent (SWGD), is also proposed. Extensive experiments based on two real-world network traffic datasets demonstrate that our model outperforms the state-of-the-art network traffic prediction models by more than 29 percent.

中文翻译:


LNTP:网络流量的端到端在线预测模型



随着网络数据不断增大,深度学习将在网络设计和管理中发挥关键作用。同时,准确的网络流量预测对于提高用户服务质量(QoS)的网络管理至关重要。然而,由于三个挑战,现有的网络流量预测方法的性能仍然很差:网络流量的复杂特征、不同网络应用引起的流量模式的动态性以及突发性等复杂的变化集。在本文中,我们提出了一种基于长短期记忆(LSTM)的网络流量预测(LNTP​​)模型,旨在及时、准确地预测网络流量。该模型可分为两部分,即小波变换和LSTM。 LNTP的工作过程分为数据采集、模型训练、在线学习与预测三个阶段。此外,为了避免突发性对模型造成的负激励并适应网络流量的变化趋势,还提出了一种神经网络权值优化算法——滑动窗口梯度下降(SWGD)。基于两个真实网络流量数据集的大量实验表明,我们的模型比最先进的网络流量预测模型高出 29% 以上。
更新日期:2024-08-22
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