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Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

A Survey on Congestion Control and Scheduling for Multipath TCP: Machine Learning vs Classical Approaches

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DOI: http://dx.doi.org/10.15439/2023F9832

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 49–61 ()

Full text

Abstract. Multipath TCP (MPTCP) has been widely used as an efficient way for communication in many applications. Data centers, smartphones, and network operators use MPTCP to balance the traffic in a network efficiently. MPTCP is an extension of TCP (Transmission Control Protocol), which provides multiple paths, leading to higher throughput and low latency. Although MPTCP has shown better performance than TCP in many applications, it has its own challenges. The network can become congested due to heavy traffic in the multiple paths (subflows) if the subflow rates are not determined correctly. Moreover, communication latency can occur if the packets are not scheduled correctly between the subflows. This paper reviews techniques to solve the above-mentioned problems based on two main approaches; non data-driven (classical) and data-driven (Machine learning) approaches. This paper compares these two approaches and highlights their strengths and weaknesses with a view to motivating future researchers in this exciting area of machine learning for communications. This paper also provides details on the simulation of MPTCP and its implementations in real environments.

References

  1. L. G. Roberts, “The evolution of packet switching,” Proceedings of the IEEE, vol. 66, no. 11, pp. 1307–1313, 1978.
  2. M. Hauben, “History of ARPANET,” Site de l’Instituto Superior de Engenharia do Porto, vol. 17, pp. 1–20, 2007.
  3. J. Postel, “Rfc0768: User Datagram Protocol,” 1980.
  4. J. Postel, “Transmission Control Protocol,” tech. rep., Information Sciences Institute, University of Southern California, 1981.
  5. S. H. Baidya and R. Prakash, “Improving the performance of multipath TCP over heterogeneous paths using slow path adaptation,” in 2014 IEEE International Conference on Communications (ICC), pp. 3222–3227, IEEE, 2014.
  6. G. Huston, “TCP in a wireless world,” IEEE Internet Computing, vol. 5, no. 2, pp. 82–84, 2001.
  7. L. Chao, C. Wu, T. Yoshinaga, W. Bao, and Y. Ji, “A brief review of multipath TCP for vehicular networks,” Sensors, vol. 21, no. 8, p. 2793, 2021.
  8. S. Hassayoun, J. Iyengar, and D. Ros, “Dynamic window coupling for multipath congestion control,” in 2011 19th IEEE International Conference on Network Protocols, pp. 341–352, IEEE, 2011.
  9. R. Khalili, N. Gast, M. Popovic, and J.-Y. Le Boudec, “MPTCP is not Pareto-optimal: Performance issues and a possible solution,” IEEE/ACM Transactions On Networking, vol. 21, no. 5, pp. 1651–1665, 2013.
  10. A. Walid, Q. Peng, J. Hwang, and S. Low, “Balanced linked adaptation congestion control algorithm for MPTCP,” Internet Engineering Task Force, Internet-Draft draft-walid-mptcp-congestion-control-04, 2016.
  11. R. K. Chaturvedi and S. Chand, “An adaptive and efficient packet scheduler for multipath TCP,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 45, pp. 349–365, 2021.
  12. T. A. Le, C. S. Hong, M. A. Razzaque, S. Lee, and H. Jung, “ecMTCP: An energy-aware congestion control algorithm for multipath TCP,” IEEE Communications Letters, vol. 16, no. 2, pp. 275–277, 2011.
  13. Y. Cao, M. Xu, and X. Fu, “Delay-based congestion control for multipath TCP,” in 2012 20th IEEE International Conference on Network Protocols (ICNP), pp. 1–10, IEEE, 2012.
  14. X. Ji, B. Han, R. Li, C. Xu, Y. Li, and J. Su, “ACCeSS: adaptive QoS-aware congestion control for multipath TCP,” in 2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS), pp. 1–10, IEEE, 2022.
  15. T. Ha, A. Masood, W. Na, and S. Cho, “Intelligent multi-path TCP congestion control for video streaming in internet of deep space things communication,” ICT Express, 2023.
  16. H. Wu, O. Alay, A. Brunstrom, G. Caso, and S. Ferlin, “Falcon: Fast and accurate multipath scheduling using offline and online learning,” arXiv preprint https://arxiv.org/abs/2201.08969, 2022.
  17. J. Han, K. Xue, J. Li, R. Zhuang, R. Li, R. Yu, G. Xue, and Q. Sun, “EdAR: An experience-driven multipath scheduler for seamless handoff in mobile networks,” IEEE Transactions on Wireless Communications, 2023.
  18. S. J. Siddiqi, F. Naeem, S. Khan, K. S. Khan, and M. Tariq, “Towards AI-enabled traffic management in multipath TCP: A survey,” Computer Communications, vol. 181, pp. 412–427, 2022.
  19. M. Y. Asiri, “A survey of multipath TCP scheduling schemes: Open challenges and potential enablers.” https://www.techrxiv.org/, 2021.
  20. P. Tomar, G. Kumar, L. P. Verma, V. K. Sharma, D. Kanellopoulos, S. S. Rawat, and Y. Alotaibi, “CMT-SCTP and MPTCP multipath transport protocols: A comprehensive review,” Electronics, vol. 11, no. 15, p. 2384, 2022.
  21. C. Xu, J. Zhao, and G.-M. Muntean, “Congestion control design for multipath transport protocols: A survey,” IEEE communications Surveys & Tutorials, vol. 18, no. 4, pp. 2948–2969, 2016.
  22. A. Jasin, R. Alsaqour, M. S. Abdelhaq, O. Alsukour, and R. Saeed, “Review on current transport layer protocols for TCP/IP model,” International Journal of Digital Content Technology and its Applications, vol. 6, no. 14, pp. 495–503, 2012.
  23. W. Wei, K. Xue, J. Han, D. S. Wei, and P. Hong, “Shared bottleneck-based congestion control and packet scheduling for multipath TCP,” IEEE/ACM Transactions on Networking, vol. 28, no. 2, pp. 653–666, 2020.
  24. S. Zannettou, M. Sirivianos, and F. Papadopoulos, “Exploiting path diversity in datacenters using MPTCP-aware SDN,” in 2016 IEEE Symposium on Computers and Communication (ISCC), pp. 539–546, IEEE, 2016.
  25. S. Hochreiter, “The vanishing gradient problem during learning recurrent neural nets and problem solutions,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 6, no. 02, pp. 107–116, 1998.
  26. Z. Xu, J. Tang, C. Yin, Y. Wang, and G. Xue, “Experience-driven congestion control: When multi-path TCP meets deep reinforcement learning,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1325–1336, 2019.
  27. B. He, J. Wang, Q. Qi, H. Sun, J. Liao, C. Du, X. Yang, and Z. Han, “DeepCC: Multi-agent deep reinforcement learning congestion control for multi-path TCP based on self-attention,” IEEE Transactions on Network and Service Management, vol. 18, no. 4, pp. 4770–4788, 2021.
  28. M. L. Puterman, “Markov decision processes,” Handbooks in Operations Research and Management Science, vol. 2, pp. 331–434, 1990.
  29. G. Tesauro et al., “Temporal difference learning and TD-Gammon,” Communications of the ACM, vol. 38, no. 3, pp. 58–68, 1995.
  30. T. Lin, Y. Wang, X. Liu, and X. Qiu, “A survey of transformers,” AI Open, 2022.
  31. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems, vol. 30, 2017.
  32. V. Pramanik and M. Maliha, “Analyzing sentiment towards a product using DistilBERT and LSTM,” in 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 811–816, IEEE, 2022.
  33. S. Deng, R. Netravali, A. Sivaraman, and H. Balakrishnan, “WiFi, LTE, or both? Measuring multi-homed wireless internet performance,” in Proceedings of the 2014 Conference on Internet Measurement Conference, pp. 181–194, 2014.
  34. S. Singh, P. Mudgal, P. Chaudhary, and A. K. Tripathi, “Comparative analysis of packet loss in extended wired LAN environment,” International Journal of Computer Applications, vol. 117, no. 2, 2015.
  35. C. Raiciu, S. Barre, C. Pluntke, A. Greenhalgh, D. Wischik, and M. Handley, “Improving datacenter performance and robustness with multipath TCP,” ACM SIGCOMM Computer Communication Review, vol. 41, no. 4, pp. 266–277, 2011.
  36. A. Greenberg, J. R. Hamilton, N. Jain, S. Kandula, C. Kim, P. Lahiri, D. A. Maltz, P. Patel, and S. Sengupta, “VL2: A scalable and flexible data center network,” in Proceedings of the ACM SIGCOMM 2009 Conference on Data Communication, pp. 51–62, 2009.
  37. “Fat-Tree Design.” https://clusterdesign.org/fat-trees/. [Accessed 19-Jul-2023].
  38. K. T. Hanna and P. Loshin, “NACK (NAK, negative acknowledgment, not acknowledged),” TechTarget, Aug 2021.
  39. F. Yang, Q. Wang, and P. D. Amer, “Out-of-order transmission for in-order arrival scheduling for multipath TCP,” in 2014 28th International Conference on Advanced Information Networking and Applications Workshops, pp. 749–752, IEEE, 2014.
  40. Y.-C. Chen, Y.-s. Lim, R. J. Gibbens, E. M. Nahum, R. Khalili, and D. Towsley, “A measurement-based study of multipath TCP performance over wireless networks,” in Proceedings of the 2013 cConference on Internet Measurement Conference, pp. 455–468, 2013.
  41. S. Ha, I. Rhee, and L. Xu, “CUBIC: a new TCP-friendly high-speed TCP variant,” ACM SIGOPS Operating Systems Review, vol. 42, no. 5, pp. 64–74, 2008.
  42. O. Ait-Hellal and E. Altman, “Analysis of TCP vegas and TCP reno,” Telecommunication Systems, vol. 15, no. 3-4, pp. 381–404, 2000.
  43. J. Yang, J. Han, K. Xue, Y. Wang, J. Li, Y. Xing, H. Yue, and D. S. Wei, “TCCC: a throughput consistency congestion control algorithm for MPTCP in mixed transmission of long and short flows,” IEEE Transactions on Network and Service Management, 2023.
  44. T. Gilad, N. Rozen-Schiff, P. B. Godfrey, C. Raiciu, and M. Schapira, “MPCC: Online learning multipath transport,” in Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies, pp. 121–135, 2020.
  45. M. H. Rehmani and Y. Saleem, “Network simulator NS-2,” in Encyclopedia of Information Science and Technology, Third Edition, pp. 6249–6258, IGI Global, 2015.
  46. T. R. Henderson, M. Lacage, G. F. Riley, C. Dowell, and J. Kopena, “Network simulations with the NS-3 simulator,” SIGCOMM Demonstration, vol. 14, no. 14, p. 527, 2008.
  47. X. Li et al., “Using” random forest” for classification and regression.,” Chinese Journal of Applied Entomology, vol. 50, no. 4, pp. 1190–1197, 2013.
  48. A. Singh, M. Xiang, A. Konsgen, C. Goerg, and Y. Zaki, “Enhancing fairness and congestion control in multipath TCP,” in 6th joint IFIP Wireless and Mobile Networking Conference (WMNC), pp. 1–8, IEEE, 2013.
  49. S. Ferlin, Ö. Alay, T. Dreibholz, D. A. Hayes, and M. Welzl, “Revisiting congestion control for multipath TCP with shared bottleneck detection,” in IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9, IEEE, 2016.
  50. J. Ahrenholz, C. Danilov, T. R. Henderson, and J. H. Kim, “CORE: A real-time network emulator,” in MILCOM 2008-2008 IEEE Military Communications Conference, pp. 1–7, IEEE, 2008.
  51. T. Zhang and S. Mao, “Machine learning for end-to-end congestion control,” IEEE Communications Magazine, vol. 58, no. 6, pp. 52–57, 2020.
  52. R. Zhuang, J. Han, K. Xue, J. Li, D. S. Wei, R. Li, Q. Sun, and J. Lu, “Achieving flexible and lightweight multipath congestion control through online learning,” IEEE Transactions on Network and Service Management, vol. 20, no. 1, pp. 46–59, 2022.
  53. A. Slivkins et al., “Introduction to multi-armed bandits,” Foundations and Trends® in Machine Learning, vol. 12, no. 1-2, pp. 1–286, 2019.
  54. D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller, “Deterministic policy gradient algorithms,” in International Conference on Machine Learning, pp. 387–395, Pmlr, 2014.
  55. W. Li, H. Zhang, S. Gao, C. Xue, X. Wang, and S. Lu, “SmartCC: A reinforcement learning approach for multipath TCP congestion control in heterogeneous networks,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 11, pp. 2621–2633, 2019.
  56. J. Xu and B. Ai, “Deep reinforcement learning for handover-aware MPTCP congestion control in space-ground integrated network of railways,” IEEE Wireless Communications, vol. 28, no. 6, pp. 200–207, 2021.
  57. Z. Xu, J. Tang, J. Meng, W. Zhang, Y. Wang, C. H. Liu, and D. Yang, “Experience-driven networking: A deep reinforcement learning based approach,” in IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pp. 1871–1879, IEEE, 2018.
  58. S. R. Pokhrel, L. Pan, N. Kumar, R. Doss, and H. L. Vu, “Multipath TCP meets transfer learning: A novel edge-based learning for industrial IoT,” IEEE Internet of Things Journal, vol. 8, no. 13, pp. 10299–10307, 2021.
  59. J. Hwang and J. Yoo, “Packet scheduling for multipath TCP,” in 2015 Seventh International Conference on Ubiquitous and Future Networks, pp. 177–179, IEEE, 2015.
  60. C. Paasch, S. Ferlin, O. Alay, and O. Bonaventure, “Experimental evaluation of multipath TCP schedulers,” in Proceedings of the 2014 ACM SIGCOMM Workshop on Capacity Sharing Workshop, pp. 27–32, 2014.
  61. M. Scharf and S. Kiesel, “NXG03-5: Head-of-line blocking in TCP and SCTP: Analysis and measurements,” in IEEE Globecom 2006, pp. 1–5, IEEE, 2006.
  62. P. Dong, J. Xie, W. Tang, N. Xiong, H. Zhong, and A. V. Vasilakos, “Performance evaluation of multipath TCP scheduling algorithms,” IEEE Access, vol. 7, pp. 29818–29825, 2019.
  63. N. Kuhn, E. Lochin, A. Mifdaoui, G. Sarwar, O. Mehani, and R. Boreli, “DAPS: Intelligent delay-aware packet scheduling for multipath transport,” in 2014 IEEE International Conference on Communications (ICC), pp. 1222–1227, IEEE, 2014.
  64. F. Yang, Q. Wang, and P. D. Amer, “Out-of-order transmission for in-order arrival scheduling for multipath TCP,” in 2014 28th International Conference on Advanced Information Networking and Applications Workshops, pp. 749–752, IEEE, 2014.
  65. S. Ferlin, Ö. Alay, O. Mehani, and R. Boreli, “BLEST: Blocking estimation-based MPTCP scheduler for heterogeneous networks,” in 2016 IFIP Networking Conference (IFIP Networking) and Workshops, pp. 431–439, IEEE, 2016.
  66. T.-A. Le and L. X. Bui, “Forward delay-based packet scheduling algorithm for multipath TCP,” Mobile Networks and Applications, vol. 23, no. 1, pp. 4–12, 2018.
  67. F. Silva, M. Togou, and G.-M. Muntean, “An innovative machine learning approach to improve MPTCP performance,” in 2020 International Conference on High Performance Computing and Simulation, IEEE, 2020.
  68. X. Su, X. Yan, and C.-L. Tsai, “Linear regression,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 4, no. 3, pp. 275–294, 2012.
  69. T. M. Mitchell, “Artificial neural networks,” Machine Learning, vol. 45, no. 81, p. 127, 1997.
  70. T. Viernickel, A. Froemmgen, A. Rizk, B. Koldehofe, and R. Steinmetz, “Multipath QUIC: A deployable multipath transport protocol,” in 2018 IEEE International Conference on Communications (ICC), pp. 1–7, IEEE, 2018.
  71. S. R. Pokhrel and A. Walid, “Learning to harness bandwidth with multipath congestion control and scheduling,” IEEE Transactions on Mobile Computing, 2021.
  72. Q. Peng, A. Walid, and S. H. Low, “Multipath TCP algorithms: Theory and design,” ACM SIGMETRICS Performance Evaluation Review, vol. 41, no. 1, pp. 305–316, 2013.
  73. B. Chihani and C. Denis, “A Multipath TCP model for ns-3 simulator,” arXiv preprint https://arxiv.org/abs/1112.1932, 2011.
  74. K. Nadeem and T. M. Jadoon, “An NS-3 MPTCP implementation,” in Quality, Reliability, Security and Robustness in Heterogeneous Systems: 14th EAI International Conference, Qshine 2018, Ho Chi Minh City, Vietnam, December 3–4, 2018, Proceedings 14, pp. 48–60, Springer, 2019.
  75. M. Coudron and S. Secci, “An implementation of multipath TCP in NS3,” Computer Networks, vol. 116, pp. 1–11, 2017.
  76. B. Y. L. Kimura and A. A. F. Loureiro, “MPTCP linux kernel congestion controls,” arXiv preprint https://arxiv.org/abs/1812.03210, 2018.
  77. M. Al-Fares, A. Loukissas, and A. Vahdat, “A scalable, commodity data center network architecture,” ACM SIGCOMM Computer Communication Review, vol. 38, no. 4, pp. 63–74, 2008.
  78. A. Singla, C.-Y. Hong, L. Popa, and P. B. Godfrey, “Jellyfish: Networking data centers randomly,” in 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12), pp. 225–238, 2012.
  79. C. Paasch and S. Barre, “Multipath TCP in the linux kernel.” available from http://www.multipath-tcp.org.