# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## 7 Conclusions and Future Work  
With the development of IPv6, it is increasingly crucial to study IPv6 traffic patterns. To this end, we analyzed the weekday/weekend patterns, the self-similarity of the IPv6 traffic, and the correlation between IPv6 and IPv4 traffic for two universities in Shanghai, China. The network traffic distribution on weekdays is not much different from that on weekends, while the traffic usage is smaller on weekends. It shows that the IPv6 users of the two campus networks are more active during weekdays. We evaluate the self-similarity by estimating the value of the Hurst exponent. The Hurst exponents of the IPv6 traffic are similar to those of IPv4 traffic, which implies that the difference between IPv6 and IPv4 traffic is smaller than before. As the smallest correlation coefficients between IPv4 traffic and IPv6 traffic are 0.699 and 0.660 for DHU and ECNU, respectively, there is a strong positive correlation between IPv6 traffic and IPv4 traffic in the two universities.  
To better predict IPv6 traffic, we proposed a new approach called LS6 by combining the advantages of LSTM and seasonal ARIMA and utilizing the correlation between IPv6 and IPv4 traffic. Based on two real-world IPv6 traffic datasets, the experiments show that LS6 achieves a lower prediction error (MAPE) than eight baselines. For example, the MAPE value of LS6 in DHU is 2.65% lower than that of the best baseline. The results confirm that our proposed model LS6 can better predict the IPv6 traffic volume than existing methods.  
Overall, our research is beneficial for ISPs to allocate and manage network resources. In our future work, we will try to obtain more IPv6 traffic volume datasets in other cities worldwide. In addition, we will further study the IPv6 traffic prediction with model combinations. We will improve the prediction performance by enhancing the LSTM model with an additional attention layer and by considering other features, such as the Hurst exponent and differences between weekdays and weekends.  
Conceptualization, Z.S. and H.R.; methodology, Y.C. ([PERSON]); software, H.R.; validation, H.R. and Y.C. ([PERSON]); formal analysis, Z.S.; investigation, Z.S.; resources, Z.S.; data curation, Y.C. ([PERSON]); writing--original draft preparation, Z.S., H.R. and Y.C. ([PERSON]); writing--review and editing, Y.C. ([PERSON]) and X.W.; visualization, H.R.; supervision, Y.C. ([PERSON]); project administration, Z.S. and Y.C. ([PERSON]); funding acquisition, Y.C. ([PERSON]) and X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 61971145).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Model** & **MAPE (24 h)** & **MAPE (Directly)** \\ \hline \multirow{6}{*}{DHU} & SVM & 0.6432 & 0.7471 \\  & LSTM & 0.4143 & 0.4557 \\  & Bi-LSTM & 0.4529 & 0.3678 \\  & PLSTM & 0.3215 & 0.7975 \\  & LS6 & 0.3998 & **0.2410** \\ \hline \multirow{6}{*}{ECNU} & SVM & 0.3992 & 0.6998 \\  & LSTM & 0.3287 & 0.3367 \\ \cline{1-1}  & Bi-LSTM & 0.3263 & 0.5479 \\ \cline{1-1}  & PLSTM & 0.3453 & 0.4509 \\ \cline{1-1}  & LS6 & 0.3428 & **0.3146** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction performance of LS6 and baseline methods in different training approaches.