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An Intelligent File Transfer Optimization for Poor Network Conditions

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Security and Privacy in Social Networks and Big Data (SocialSec 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1298))

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Abstract

File transfer is based on the reliable TCP protocol. However, when the network is of poor quality, TCP-based transmission will still perform less effectively due to some reasons. Existing approaches mainly optimize file transmission by modifying network strategies or optimize transmission mechanism. But when network conditions cannot be selected or modified, a suitable parameter adjustment method would be needed. The experiments in this paper firstly perform TCP-based file transmission tests on a poor network condition and find two problems. Then, multiple tests were performed on multiple platforms to detect the conditions under which these problems occurred. Next, to address the issue and improve the transmission performance, we propose an intelligent optimization scheme. By adjusting the transmission parameters and adding policies, the scheme equips the intermediate parameters with intelligent self-adaptation capabilities. We also test and evaluate the performance of the intelligent scheme. The result shows that the file transfer under the new scheme not only basically avoids the target problems, but also reduces the practical upload time under poor network condition from the perspective of decreasing the number of retransmissions and reducing the failure rate.

Supported by the National Natural Science Foundation of China under Grant No. 61876019 and Zhejiang Lab (NO. 2020LE0AB02).

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Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grant No. 61876019 and Zhejiang Lab (NO. 2020LE0AB02).

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Correspondence to Ruyun Zhang .

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Yan, M., Zhang, B., Zhao, G., Kuang, X., Liu, L., Zhang, R. (2020). An Intelligent File Transfer Optimization for Poor Network Conditions. In: Xiang, Y., Liu, Z., Li, J. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2020. Communications in Computer and Information Science, vol 1298. Springer, Singapore. https://doi.org/10.1007/978-981-15-9031-3_21

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  • DOI: https://doi.org/10.1007/978-981-15-9031-3_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-9030-6

  • Online ISBN: 978-981-15-9031-3

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