Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
With the exhaustion of IPv4 addresses, research on the adoption, deployment, and prediction of IPv6 networks becomes more and more significant. This paper analyzes the IPv6 traffic of two campus networks in Shanghai, China. We first conduct a series of analyses for the traffic patterns and uncover w...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/14/12/353 |
| _version_ | 1797458217285976064 |
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| author | Zhiyang Sun Hui Ruan Yixin Cao Yang Chen Xin Wang |
| author_facet | Zhiyang Sun Hui Ruan Yixin Cao Yang Chen Xin Wang |
| author_sort | Zhiyang Sun |
| collection | DOAJ |
| description | With the exhaustion of IPv4 addresses, research on the adoption, deployment, and prediction of IPv6 networks becomes more and more significant. This paper analyzes the IPv6 traffic of two campus networks in Shanghai, China. We first conduct a series of analyses for the traffic patterns and uncover weekday/weekend patterns, the self-similarity phenomenon, and the correlation between IPv6 and IPv4 traffic. On weekends, traffic usage is smaller than on weekdays, but the distribution does not change much. We find that the self-similarity of IPv4 traffic is close to that of IPv6 traffic, and there is a strong positive correlation between IPv6 traffic and IPv4 traffic. Based on our findings on traffic patterns, we propose a new IPv6 traffic prediction model by combining the advantages of the statistical and deep learning models. In addition, our model would extract useful information from the corresponding IPv4 traffic to enhance the prediction. Based on two real-world datasets, it is shown that the proposed model outperforms eight baselines with a lower prediction error. In conclusion, our approach is helpful for network resource allocation and network management. |
| first_indexed | 2024-03-09T16:33:54Z |
| format | Article |
| id | doaj.art-02cc73ee119b4088a3a4405d65bda147 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T16:33:54Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-02cc73ee119b4088a3a4405d65bda1472023-11-24T14:58:23ZengMDPI AGFuture Internet1999-59032022-11-01141235310.3390/fi14120353Analysis and Prediction of the IPv6 Traffic over Campus Networks in ShanghaiZhiyang Sun0Hui Ruan1Yixin Cao2Yang Chen3Xin Wang4School of Information Science and Technology, Fudan University, Shanghai 200438, ChinaShanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200438, ChinaShanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200438, ChinaShanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200438, ChinaShanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200438, ChinaWith the exhaustion of IPv4 addresses, research on the adoption, deployment, and prediction of IPv6 networks becomes more and more significant. This paper analyzes the IPv6 traffic of two campus networks in Shanghai, China. We first conduct a series of analyses for the traffic patterns and uncover weekday/weekend patterns, the self-similarity phenomenon, and the correlation between IPv6 and IPv4 traffic. On weekends, traffic usage is smaller than on weekdays, but the distribution does not change much. We find that the self-similarity of IPv4 traffic is close to that of IPv6 traffic, and there is a strong positive correlation between IPv6 traffic and IPv4 traffic. Based on our findings on traffic patterns, we propose a new IPv6 traffic prediction model by combining the advantages of the statistical and deep learning models. In addition, our model would extract useful information from the corresponding IPv4 traffic to enhance the prediction. Based on two real-world datasets, it is shown that the proposed model outperforms eight baselines with a lower prediction error. In conclusion, our approach is helpful for network resource allocation and network management.https://www.mdpi.com/1999-5903/14/12/353IPv6 traffic analysisself-similarityIPv6 traffic predictionSARIMALSTM |
| spellingShingle | Zhiyang Sun Hui Ruan Yixin Cao Yang Chen Xin Wang Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai Future Internet IPv6 traffic analysis self-similarity IPv6 traffic prediction SARIMA LSTM |
| title | Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai |
| title_full | Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai |
| title_fullStr | Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai |
| title_full_unstemmed | Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai |
| title_short | Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai |
| title_sort | analysis and prediction of the ipv6 traffic over campus networks in shanghai |
| topic | IPv6 traffic analysis self-similarity IPv6 traffic prediction SARIMA LSTM |
| url | https://www.mdpi.com/1999-5903/14/12/353 |
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