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ADAS: Adaptive Delay-Aligned Scheduling for Multipath Transmission in Heterogeneous Wireless Networks

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Abstract

Multipath TCP (MPTCP) is regarded as a promising solution to aggregate the bandwidth of multiple paths to generate throughput benefits in heterogeneous networks. However, transmitting data over multiple paths simultaneously often leads to out-of-order issues due to the asymmetry of heterogeneous paths. In this paper, we propose a novel packet scheduling mechanism named Adaptive Delay-Aligned Scheduling (ADAS) for multipath transmission in heterogeneous wireless networks. ADAS utilizes the wisdom of the last-hop connected to the receiver to solve the out-of-order problem and improve the overall throughput at the same time. Specifically, ADAS equips a virtual link loop on the last-hop to buffer the out-of-order packets within time threshold and further schedules and sends them to the receiver as sequentially as possible, which looks as if all packets take the same time to travel across the network. In this way, the delay-aligned scheduling is achieved and the out-of-order problem can be effectively addressed. Besides, an adaptive weighting algorithm is proposed to dynamically adjust the time threshold to avoid over scheduling and improve the overall throughput. Extensive experiments demonstrate that ADAS outperforms state-of-the-art mechanisms. Besides, a lower out-of-order rate of 5.73% and a higher overall throughput of 6.76 Mbps can be achieved through combining ADAS with the current scheduling mechanisms.

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References

  1. Wang D, Ding W, Ma X, Jiang H, Wang F, Liu J (2019) MiFo: a novel edge network integration framework for fog computing. Peer-to-peer Netw Appl 12(1):269–279. https://doi.org/10.1007/s12083-018-0663-z

    Article  Google Scholar 

  2. Zhong L, Ji X, Wang Z, Qin J, Muntean GM (2022) A Q-learning driven energy-aware multipath transmission solution for 5G media services. IEEE Trans Broadcast 68(2):559–571. https://doi.org/10.1109/TBC.2022.3147098

    Article  Google Scholar 

  3. Wang J, Liao J, Li T, Wang J (2015) On the collaborations of multiple selfish overlays using multi-path resources. Peer-to-Peer Netw Appl 8(2):203–215. https://doi.org/10.1007/s12083-013-0245-z

    Article  Google Scholar 

  4. Ford A, Raiciu C, Handley M, Barre S, Iyengar J (2011) Architectural guidelines for multipath TCP development. https://datatracker.ietf.org/doc/rfc6182/

  5. Morawski M, Ignaciuk P (2021) Choosing a proper control strategy for multipath transmission in industry 4.0 applications. IEEE Trans Ind Inform 18(6):3609–3619. https://doi.org/10.1109/TII.2021.3105499

  6. Yu C, Quan W, Liu K, Liu M, Xu Z, Zhang H (2022) DRL-based fountain codes for concurrent multipath transfer in 6G networks. In: Proceedings of 2022 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), New York, NY, USA, pp 1–6. https://doi.org/10.1109/INFOCOMWKSHPS54753.2022.9798044

  7. Kimura BY, Lima DC, Loureiro AA (2020) Packet scheduling in multipath TCP: Fundamentals, lessons, and opportunities. IEEE Syst J 15(1):1445–1457. https://doi.org/10.1109/JSYST.2020.2965471

    Article  Google Scholar 

  8. Ferlin S, Alay Ö, Mehani O, Boreli R (2016) BLEST: Blocking estimation-based MPTCP scheduler for heterogeneous networks. In: Proceedings of 2016 IFIP Networking Conference (IFIP Networking) and Workshops, Vienna, Austria, pp 431–439. https://doi.org/10.1109/IFIPNetworking.2016.7497206

  9. Xu C, Li Z, Zhong L, Zhang H, Muntean GM (2015) CMT-NC: Improving the concurrent multipath transfer performance using network coding in wireless networks. IEEE Trans Veh Technol 65(3):1735–1751. https://doi.org/10.1109/TVT.2015.2409556

    Article  Google Scholar 

  10. Xue K, Han J, Zhang H, Chen K, Hong P (2016) Migrating unfairness among subflows in MPTCP with network coding for wired-wireless networks. IEEE Trans Veh Technol 66(1):798–809. https://doi.org/10.1109/TVT.2016.2543842

    Article  Google Scholar 

  11. Paasch C, Khalili R, Bonaventure O (2013) On the benefits of applying experimental design to improve multipath TCP. In: Proceedings of the 9th ACM Conference on Emerging Networking Experiments and Technologies (CoNEXT), Santa Barbara, California, USA, pp 393–398. https://doi.org/10.1145/2535372.2535403

  12. Paasch C, Ferlin S, Alay O, Bonaventure O (2014) Experimental evaluation of multipath TCP schedulers. In: Proceedings of 2014 ACM SIGCOMM workshop on Capacity sharing workshop (CSWS), Chicago, Illinois, USA, pp 27–32. https://doi.org/10.1145/2630088.2631977

  13. Kimura BY, Lima DC, Loureiro AA (2017) Alternative scheduling decisions for multipath TCP. IEEE Commun Lett 21(11):2412–2415. https://doi.org/10.1109/LCOMM.2017.2740918

    Article  Google Scholar 

  14. Sarwar G, Boreli R, Lochin E, Mifdaoui A, Smith G (2013) Mitigating receiver’s buffer blocking by delay aware packet scheduling in multipath data transfer. In: Proceedings of 2013 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA), Barcelona, Spain, pp 1119–1124. https://doi.org/10.1109/WAINA.2013.80

  15. Kuhn N, Lochin E, Mifdaoui A, Sarwar G, Mehani O, Boreli R (2014) DAPS: Intelligent delay-aware packet scheduling for multipath transport. In: Proceedings of 2014 IEEE International Conference on Communications (ICC), Sydney, NSW, Australia, pp 1222–1227. https://doi.org/10.1109/ICC.2014.6883488

  16. Yang F, Wang Q, Amer PD (2014) Out-of-order transmission for in-order arrival scheduling for multipath TCP. In: Proceedings of 2014 28th International Conference on Advanced Information Networking and Applications Workshops (WAINA), Victoria, BC, Canada, pp 749–752. https://doi.org/10.1109/WAINA.2014.122

  17. Ke F, Huang M, Liu Z, Liu Q, Cao Y (2016) Multi-attribute aware multipath data scheduling strategy for efficient MPTCP-based data delivery. In: Proceedings of 2016 22nd Asia-Pacific Conference on Communications (APCC), Yogyakarta, Indonesia, pp 248–253. https://doi.org/10.1109/APCC.2016.7581457

  18. Luo J, Su X, Liu B, Zeng J (2018) Multi-attribute aware data scheduling for multipath TCP. In: Proceedings of 2018 18th International Symposium on Communications and Information Technologies (ISCIT), Bangkok, Thailand, pp 270–274. https://doi.org/10.1109/ISCIT.2018.8587933

  19. Xue K, Han J, Ni D, Wei W, Cai Y, Xu Q, Hong P (2017) DPSAF: Forward prediction based dynamic packet scheduling and adjusting with feedback for multipath TCP in lossy heterogeneous networks. IEEE Trans Veh Technol 67(2):1521–1534. https://doi.org/10.1109/TVT.2017.2753398

    Article  Google Scholar 

  20. Dong E, Xu M, Fu X, Cao Y (2019) A loss aware MPTCP scheduler for highly lossy networks. Comput Netw 157:146–158. https://doi.org/10.1016/j.comnet.2019.02.001

    Article  Google Scholar 

  21. Yang W, Dong P, Cai L, Tang W (2021) Loss-aware throughput estimation scheduler for multi-path TCP in heterogeneous wireless networks. IEEE Trans Wirel Commun 20(5):3336–3349. https://doi.org/10.1109/TWC.2021.3049300

    Article  Google Scholar 

  22. Jiang H, Li Q, Jiang Y, Shen G, Sinnott R, Tian C, Xu M (2022) When machine learning meets congestion control: a survey and comparison. Comput Netw 192:108033. https://doi.org/10.1016/j.comnet.2021.108033

    Article  Google Scholar 

  23. Siddiqi SJ, Naeem F, Khan S, Khan KS, Tariq M (2022) Towards AI-enabled traffic management in multipath TCP: a survey. Comput Commun 181:412–427. https://doi.org/10.1016/j.comcom.2021.09.030

    Article  Google Scholar 

  24. Zhang H, Li W, Gao S, Wang X, Ye B (2019) ReLeS: a neural adaptive multipath scheduler based on deep reinforcement learning. In: Proceedings of 2019 IEEE Conference on Computer Communications (INFOCOM), Paris, France, pp 1648–1656. https://doi.org/10.1109/INFOCOM.2019.8737649

  25. Roselló MM (2019) Multi-path scheduling with deep reinforcement learning. In: Proceedings of 2019 European Conference on Networks and Communications (EuCNC), Valencia, Spain, pp 400–405. https://doi.org/10.1109/EuCNC.2019.8802063

  26. Yu C, Quan W, Gao D, Zhang Y, Liu K, Wu W, Zhang H, Shen X (2021) Reliable cybertwin-driven concurrent multipath transfer with deep reinforcement learning. IEEE Internet Things J 8(22):16207–16218. https://doi.org/10.1109/JIOT.2021.3101447

    Article  Google Scholar 

  27. Li W, Zhang H, Gao S, Xue C, Wang X, Lu S (2019) SmartCC: a reinforcement learning approach for multipath TCP congestion control in heterogeneous networks. IEEE J Sel Areas in Commun 37(11):2621–2633. https://doi.org/10.1109/JSAC.2019.2933761

    Article  Google Scholar 

  28. Wu H, Alay Ö, Brunstrom A, Ferlin S, Caso G (2020) Peekaboo: Learning-based multipath scheduling for dynamic heterogeneous environments. IEEE J Sel Areas Commun 38(10):2295–2310. https://doi.org/10.1109/JSAC.2020.3000365

    Article  Google Scholar 

  29. Naeem F, Srivastava G, Tariq M (2020) A software defined network based fuzzy normalized neural adaptive multipath congestion control for the internet of things. IEEE Trans Netw Sci Eng 7(4):2155–2164. https://doi.org/10.1109/TNSE.2020.2991106

    Article  Google Scholar 

  30. Ji R, Cao Y, Fan X, Jiang Y, Lei G, Ma Y (2020) Multipath TCP-based IoT communication evaluation: From the perspective of multipath management with machine learning. Sensors 20(22):6573. https://doi.org/10.3390/s20226573

    Article  Google Scholar 

  31. Pokhrel SR, Garg S (2020) Multipath communication with deep Q-network for industry 4.0 automation and orchestration. IEEE Trans Ind Inform 17(4):2852–2859. https://doi.org/10.1109/TII.2020.3000502

  32. Pokhrel SR, Pan L, Kumar N, Doss R, Vu HL (2021) Multipath TCP meets transfer learning: a novel edge-based learning for industrial IoT. IEEE Internet Things J 8(13):10299–10307. https://doi.org/10.1109/JIOT.2021.3056466

    Article  Google Scholar 

  33. Cao Y, Ji R, Ji L, Lei G, Wang H, Shao X, (Early Access, (2022) \(l^2\)-MPTCP: a learning-driven latency-aware multipath transport scheme for industrial internet applications. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2022.3151093

  34. Kanagarathinam MR, Natarajan H, Arunachalam K, Sandeep I, Sunil V (2020) SMS: Smart multipath switch for improving the throughput of multipath TCP for smartphones. In: Proceedings of 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea (South), pp 1–6. https://doi.org/10.1109/WCNC45663.2020.9120463

  35. Xu C, Qin J, Zhang P, Gao K, Grieco LA, (2021) Reinforcement learning-based Mobile AR/VR Multipath Transmission with Streaming Power Spectrum Density Analysis. IEEE Transactions on Mobile Computing. https://doi.org/10.1109/TMC.2021.3082912

  36. Padhye J, Firoiu V, Towsley D, Kurose J (1998) Modeling TCP throughput: a simple model and its empirical validation. In: Proceedings of the ACM SIGCOMM’98 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication (SIGCOMM), Vancouver, British Columbia, Canada, pp 303–314. https://doi.org/10.1145/285237.285291

  37. Dong P, Yang W, Tang W, Huang J, Wang H, Pan Y, Wang J (2018) Reducing transport latency for short flows with multipath TCP. J Netw Comput Appl 108:20–36. https://doi.org/10.1016/j.jnca.2018.02.005

    Article  Google Scholar 

  38. Sargent M, Chu J, Paxson DV, Allman M (2011) Computing TCP’s retransmission timer. https://datatracker.ietf.org/doc/rfc6298/

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Funding

This work is supported by the National Natural Science Foundation of China (grant no.61971028) and the National Key Research and Development Program of China (grant no.2018YFE0206800).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing, 100044, China

    Du Chen, Deyun Gao, Lu Jin, Wei Quan & Hongke Zhang

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  1. Du Chen
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  2. Deyun Gao
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  3. Lu Jin
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  5. Hongke Zhang
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Contributions

Du Chen wrote the main manuscript text and performed the analysis. Deyun Gao assisted in the analysis and manuscript preparation. Lu Jin established the testbed and carried out experiments. Wei Quan and Hongke Zhang provided constructive suggestions on the improvement of the manuscript. All authors reviewed the manuscript.

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Correspondence to Deyun Gao.

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Chen, D., Gao, D., Jin, L. et al. ADAS: Adaptive Delay-Aligned Scheduling for Multipath Transmission in Heterogeneous Wireless Networks. Peer-to-Peer Netw. Appl. 16, 1583–1595 (2023). https://doi.org/10.1007/s12083-023-01468-y

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