MPTCP Throughput Enhancement by Q-learning for Mobile Devices

Esmaeil F. G. M. Beig, Parisa Daneshjoo, Saeid Rezaei, Ali Akbar Movassagh, Ramin Karimi, Yongrui Qin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Mobile devices are able to leverage diverse heterogeneous network paths by Multi-Path Transmission Control Protocol (MPTCP); nevertheless, boosting MPTCP throughput in wireless networks is a real bear. Not only the best path(s) should be selected, but also the optimal congestion control mechanism should be chosen. We investigate the impact of different paths and congestion control for different signal quality states. Consequently, we present the novel MPTCP algorithm augmenting the end user throughput by understating the best policy in different situations by Q-learning. The Results reveal a tremendous effect of switching between the different interfaces and changing the congestion control mechanism on throughput and delay. By and large, the proposed framework achieves 10% more throughput compared to base MPTCP.
LanguageEnglish
Title of host publication2018 IEEE 20th International Conference on High Performance Computing and Communications
Subtitle of host publicationIEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
PublisherIEEE
Pages1171-1176
Number of pages6
ISBN (Electronic)9781538666142
ISBN (Print)9781538666159
DOIs
Publication statusPublished - 24 Jan 2019
Event20th IEEE International Conference on High Performance Computing and Communications - Exeter, United Kingdom
Duration: 28 Jun 201830 Jun 2018
Conference number: 20
http://cse.stfx.ca/~hpcc2018/ (Link to Conference Website)

Conference

Conference20th IEEE International Conference on High Performance Computing and Communications
Abbreviated titleHPCC-2018
CountryUnited Kingdom
CityExeter
Period28/06/1830/06/18
Internet address

Fingerprint

Transmission control protocol
Mobile devices
Throughput
Heterogeneous networks
Wireless networks

Cite this

Beig, E. F. G. M., Daneshjoo, P., Rezaei, S., Movassagh, A. A., Karimi, R., & Qin, Y. (2019). MPTCP Throughput Enhancement by Q-learning for Mobile Devices. In 2018 IEEE 20th International Conference on High Performance Computing and Communications: IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (pp. 1171-1176). IEEE. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00197
Beig, Esmaeil F. G. M. ; Daneshjoo, Parisa ; Rezaei, Saeid ; Movassagh, Ali Akbar ; Karimi, Ramin ; Qin, Yongrui. / MPTCP Throughput Enhancement by Q-learning for Mobile Devices. 2018 IEEE 20th International Conference on High Performance Computing and Communications: IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2019. pp. 1171-1176
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title = "MPTCP Throughput Enhancement by Q-learning for Mobile Devices",
abstract = "Mobile devices are able to leverage diverse heterogeneous network paths by Multi-Path Transmission Control Protocol (MPTCP); nevertheless, boosting MPTCP throughput in wireless networks is a real bear. Not only the best path(s) should be selected, but also the optimal congestion control mechanism should be chosen. We investigate the impact of different paths and congestion control for different signal quality states. Consequently, we present the novel MPTCP algorithm augmenting the end user throughput by understating the best policy in different situations by Q-learning. The Results reveal a tremendous effect of switching between the different interfaces and changing the congestion control mechanism on throughput and delay. By and large, the proposed framework achieves 10{\%} more throughput compared to base MPTCP.",
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Beig, EFGM, Daneshjoo, P, Rezaei, S, Movassagh, AA, Karimi, R & Qin, Y 2019, MPTCP Throughput Enhancement by Q-learning for Mobile Devices. in 2018 IEEE 20th International Conference on High Performance Computing and Communications: IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, pp. 1171-1176, 20th IEEE International Conference on High Performance Computing and Communications, Exeter, United Kingdom, 28/06/18. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00197

MPTCP Throughput Enhancement by Q-learning for Mobile Devices. / Beig, Esmaeil F. G. M.; Daneshjoo, Parisa; Rezaei, Saeid; Movassagh, Ali Akbar; Karimi, Ramin; Qin, Yongrui.

2018 IEEE 20th International Conference on High Performance Computing and Communications: IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2019. p. 1171-1176.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Rezaei, Saeid

AU - Movassagh, Ali Akbar

AU - Karimi, Ramin

AU - Qin, Yongrui

N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

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N2 - Mobile devices are able to leverage diverse heterogeneous network paths by Multi-Path Transmission Control Protocol (MPTCP); nevertheless, boosting MPTCP throughput in wireless networks is a real bear. Not only the best path(s) should be selected, but also the optimal congestion control mechanism should be chosen. We investigate the impact of different paths and congestion control for different signal quality states. Consequently, we present the novel MPTCP algorithm augmenting the end user throughput by understating the best policy in different situations by Q-learning. The Results reveal a tremendous effect of switching between the different interfaces and changing the congestion control mechanism on throughput and delay. By and large, the proposed framework achieves 10% more throughput compared to base MPTCP.

AB - Mobile devices are able to leverage diverse heterogeneous network paths by Multi-Path Transmission Control Protocol (MPTCP); nevertheless, boosting MPTCP throughput in wireless networks is a real bear. Not only the best path(s) should be selected, but also the optimal congestion control mechanism should be chosen. We investigate the impact of different paths and congestion control for different signal quality states. Consequently, we present the novel MPTCP algorithm augmenting the end user throughput by understating the best policy in different situations by Q-learning. The Results reveal a tremendous effect of switching between the different interfaces and changing the congestion control mechanism on throughput and delay. By and large, the proposed framework achieves 10% more throughput compared to base MPTCP.

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Beig EFGM, Daneshjoo P, Rezaei S, Movassagh AA, Karimi R, Qin Y. MPTCP Throughput Enhancement by Q-learning for Mobile Devices. In 2018 IEEE 20th International Conference on High Performance Computing and Communications: IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE. 2019. p. 1171-1176 https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00197