  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.
