论文标题
通过有限的培训数据，基于转移学习的有效流量预测
Transfer Learning Based Efficient Traffic Prediction with Limited Training Data
论文作者
论文摘要
互联网流量的有效预测是自我组织网络（SON）的重要组成部分，以确保积极管理。使用深度学习，有许多现有的互联网流量预测解决方案。但是，由于数据异质性，稀缺性和异常，为网络中的每个服务提供商设计单个预测模型是具有挑战性的。此外，在当前的工作中尚未对网络流量预测中深层序列模型的性能进行有限的培训数据。在本文中，我们研究并评估了深度转移学习技术在交通预测中的性能，而历史数据不足，从而利用了我们的预训练模型的知识。 First, we used a comparatively larger real-world traffic dataset for source domain prediction based on five different deep sequence models: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM Encoder-Decoder (LSTM_En_De), LSTM_En_De with Attention layer (LSTM_En_De_Atn), and Gated Recurrent Unit (GRU).然后，对于目标域预测，考虑了两个最佳表现模型，即LSTM_EN_DE和LSTM_EN_DE_ATN，精度为96.06％和96.05％。最后，在目标域中使用了四个针对四个特定来源和目标对的较小的流量数据集，以比较标准学习和转移学习的性能，以准确性和执行时间为单位。根据我们的实验结果，转移学习有助于减少大多数情况下的执行时间，而模型的准确性在通过更大的培训课程中提高了转移学习。
Efficient prediction of internet traffic is an essential part of Self Organizing Network (SON) for ensuring proactive management. There are many existing solutions for internet traffic prediction with higher accuracy using deep learning. But designing individual predictive models for each service provider in the network is challenging due to data heterogeneity, scarcity, and abnormality. Moreover, the performance of the deep sequence model in network traffic prediction with limited training data has not been studied extensively in the current works. In this paper, we investigated and evaluated the performance of the deep transfer learning technique in traffic prediction with inadequate historical data leveraging the knowledge of our pre-trained model. First, we used a comparatively larger real-world traffic dataset for source domain prediction based on five different deep sequence models: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM Encoder-Decoder (LSTM_En_De), LSTM_En_De with Attention layer (LSTM_En_De_Atn), and Gated Recurrent Unit (GRU). Then, two best-performing models, LSTM_En_De and LSTM_En_De_Atn, from the source domain with an accuracy of 96.06% and 96.05% are considered for the target domain prediction. Finally, four smaller traffic datasets collected for four particular sources and destination pairs are used in the target domain to compare the performance of the standard learning and transfer learning in terms of accuracy and execution time. According to our experimental result, transfer learning helps to reduce the execution time for most cases, while the model's accuracy is improved in transfer learning with a larger training session.
