Coupled or Uncoupled? Multi-path TCP Congestion Control for High-Speed Railway Networks

被引:3
|
作者
Yu, Chengxiao [1 ]
Quan, Wei [1 ]
Cheng, Nan [2 ]
Chen, Shihua [1 ]
Zhang, Hongke [1 ]
机构
[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing, Peoples R China
[2] Xidian Univ, Sch Telecommun, Xian, Peoples R China
来源
2019 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC) | 2019年
关键词
Multi-path TCP; High Speed Railway; congestion control algorithm; PERFORMANCE; TRANSMISSION;
D O I
10.1109/iccchina.2019.8855811
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
With the development of modern High-Speed Railway (HSR) and mobile communication systems, network operators have a strong demand to provide high-quality on-hoard Internet services for IISR passengers. Multi-path TCP (MPTCP) provides a potential solution to aggregate available network bandwidth, greatly overcoming throughout degradation and severe jitter using single transmission path during the high-speed train moving. However, the choose of MPTCP algorithms, i.e., Coupled or Uncoupled, has a great impact on the performance. In this paper, we investigate this interesting issue in the practical datasets along multiple IISR lines. Particularly, we collect the first-hand network datasets and analyze the characteristics and category of traffic flows. Based on this statistics, we measure and analyze the transmission performance for both mice flows and elephant ones with different MPTCP congestion control algorithms in HSR scenarios. The simulation results show that, by comparing with the coupled MPTCP algorithms. i.e., Fully Coupled and LIA, the uncoupled EWTCP algorithm provides more stable throughput and balances congestion window distribution, more suitable for the FISK scenario for elephant flows. This work provides significant reference for the development of on-board devices in HSR network systems.
引用
收藏
页数:6
相关论文
共 50 条
  • [21] Cooperative multiagent congestion control for high-speed networks
    Hwang, KS
    Tan, SW
    Hsiao, MC
    Wu, CS
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2005, 35 (02): : 255 - 268
  • [22] End to end congestion control in high-speed networks
    Jagannathan, S
    LCN 2002: 27TH ANNUAL IEEE CONFERENCE ON LOCAL COMPUTER NETWORKS, PROCEEDINGS, 2002, : 547 - 556
  • [23] CONGESTION CONTROL IN HIGH-SPEED PACKET SWITCHED NETWORKS
    SOHRABY, K
    FRATTA, L
    GOPAL, I
    LAZAR, AA
    IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 1991, 9 (07) : 965 - 967
  • [24] A Measurement Study on Multi-path TCP with Multiple Cellular Carriers on High Speed Rails
    Li, Li
    Xu, Ke
    Li, Tong
    Zheng, Kai
    Peng, Chunyi
    Wang, Dan
    Wang, Xiangxiang
    Shen, Meng
    Mijumbi, Rashid
    PROCEEDINGS OF THE 2018 CONFERENCE OF THE ACM SPECIAL INTEREST GROUP ON DATA COMMUNICATION (SIGCOMM '18), 2018, : 161 - 175
  • [25] An In-depth Analysis of Subflow Degradation for Multi-path TCP on High Speed Rails
    Li, Tong
    Li, Li
    Wang, Xiangxiang
    Zhang, Xu
    Zhang, Feng
    Wan, Kao
    2022 IEEE 23RD INTERNATIONAL SYMPOSIUM ON A WORLD OF WIRELESS, MOBILE AND MULTIMEDIA NETWORKS (WOWMOM 2022), 2022, : 231 - 240
  • [26] Stability of multi-path dual congestion control algorithms
    Voice, Thomas
    IEEE-ACM TRANSACTIONS ON NETWORKING, 2007, 15 (06) : 1231 - 1239
  • [27] Revisiting legacy high-speed TCP congestion control variants: An optimisation-theoretic analysis of multi-mode TCP
    Edwan, Talal A.
    Phillips, Iain W.
    Guan, Lin
    Crowcroft, Jon
    Tahat, Ashraf
    Badr, Bashar E. A.
    SIMULATION MODELLING PRACTICE AND THEORY, 2022, 118
  • [28] Intelligent Multi-Path TCP Congestion Control for video streaming in Internet of Deep Space Things communication
    Ha, Taeyun
    Masood, Arooj
    Na, Woongsoo
    Cho, Sungrae
    ICT EXPRESS, 2023, 9 (05): : 860 - 868
  • [29] Experience-Driven Congestion Control: When Multi-Path TCP Meets Deep Reinforcement Learning
    Xu, Zhiyuan
    Tang, Jian
    Yin, Chengxiang
    Wang, Yanzhi
    Xue, Guoliang
    IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2019, 37 (06) : 1325 - 1336
  • [30] Improving the Congestion Control Performance for Mobile Networks in High-Speed Railway via Deep Reinforcement Learning
    Cui, Laizhong
    Yuan, Zuxian
    Ming, Zhongxing
    Yang, Shu
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (06) : 5864 - 5875