SVM-based loss differentiation algorithm for wir ed-cum-wireless networks

doi:10.1016/S1005-8885(08)60256-3

中国邮电高校学报(英文) ›› 2009, Vol. 16 ›› Issue (4): 104-111.doi: 10.1016/S1005-8885(08)60256-3

SVM-based loss differentiation algorithm for wir ed-cum-wireless networks

邓钱华,蔡安妮

School of Telecommunication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

收稿日期:2008-06-02 修回日期:1900-01-01 出版日期:2009-08-31
通讯作者: 邓钱华

SVM-based loss differentiation algorithm for wir ed-cum-wireless networks

DENG Qian-hua, CAI An-ni

School of Telecommunication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2008-06-02 Revised:1900-01-01 Online:2009-08-31
Contact: DENG Qian-hua

摘要/Abstract

摘要：

In a hybrid wired-cum-wireless network environment, packet loss may happen because of congestion or wireless link errors. Therefore, differentiating the cause is important for helping transport protocols take actions to control congestion only when the loss is caused by congestion. In this article, an end-to-end loss differentiation mechanism is proposed to improve the transmission performance of transmission control protocol (TCP)-friendly rate control (TFRC) protocol. Its key design is the introduction of the outstanding machine learning algorithm – the support vector machine (SVM) into the network domain to perform multi-metric joint loss differentiation. The SVM is characterized by using end-to-end indicators for input, such as the relative one-way trip time and the inter-arrival time of packets fore-and-aft the loss, while requiring no support from intermediate network apparatus. Simulations are carried out to evaluate the loss differentiation algorithm with various network configurations, such as with different competing flows, wireless loss rate and queue size. The results show that the proposed classifier is effective under most scenarios, and that its performance is superior to the ZigZag, mBiaz and spike (ZBS) scheme.

关键词:

machine;learning,;SVM,;TCP-friendly;rate;control;(TFRC),;congestion;control

Abstract:

Key words:

machine learning;SVM;TCP-friendly rate control (TFRC);congestion control

DENG Qian-hua, CAI An-ni. SVM-based loss differentiation algorithm for wir ed-cum-wireless networks[J]. Acta Metallurgica Sinica(English letters), 2009, 16(4): 104-111.

参考文献

1. Stefano B, Davide C, Fabio M. Enhanced loss differentiation algorithms for use in TCP sources over heterogeneous wireless networks. Proceedings of the Global Telecommunications Conference (GLOBECOM’03): Vol 2, Dec 1-5, 2003, San Francisco, CA, USA. Piscataway, NJ, USA: IEEE, 2003: 666-670

2. Handley M, Floyd S, Padhye J, et al. TCP friendly rate control (TFRC): Protocol specification. RFC3448. 2003

3. Tobe Y, Tamura Y, Molano A, et al. Achieving moderate fairness for UDP flows by path-status classification. Proceedings of the 25th Annual IEEE Conference on Local Computer Networks (LCN’00), Nov 8-10, 2000, Tampa, FL, USA. Los Alamitos, CA, USA: IEEE Computer Society, 2000: 252-261

4. Bakre A V, Badrinath B R. Implementation and performance evaluation of indirect TCP. IEEE Transactions on Computers, 1997, 46( 3): 260-278

5. Floyd S. TCP and explicit congestion notification. ACM Computer Communication Review, 1994, 24(5): 10-23

6. Biaz S, Vaidya N H. Discriminating congestion losses from wireless losses using inter-arrival times at the receiver. Proceedings of the IEEE

Symposium on Application-specific Systems and Software Engineering and Technology (ASSET’99), Mar 24-27, 1999, Richardson, TX, USA. Piscataway, NJ, USA: IEEE, 1999: 10-17

7. Cen S, Cosman P C, Voelker G M. End-to-end differentiation of congestion and wireless losses. IEEE/ACM Transactions on Networking, 2003, 11(5): 703-717

8. Park M K, Sihn K H, Jeong J H. A statistical method of packet loss type discrimination in wired-wireless networks. Proceedings of the 3rd IEEE Consumer Communications and Networking Conference (CCNC’06), Jan 8-10, 2006, Las Vegas, NA, USA. Piscataway, NJ, USA: IEEE, 2006: 458-462

9. Brakmo L S, O’Malley S W, Peterson L L. TCP vegas: New techniques for congestion detection and avoidance. Proceedings of Conference on Applications, Technologies, Architectures and Protocols for Computer Communication (SIGCOMM’94), Aug 31-Sep 2, 1994, London, UK. New York, NY, USA: ACM, 1994: 24-35

10. Liu J, Matta I, Crovella M. End-to-end inference of loss nature in a hybrid wired/wireless networks. Proceedings of the 1st Workshop on Modeling and Optimization in Mobile, Ad-hoc and Wireless Networks (WiOpt’03), Mar 3-5, 2003, Sophia-Antipolis, France. 2003: 9

11. Fu Z H, Greentein B, Meng X Q, et al. Design and implementation of a TCP-friendly transport protocol for ad hoc wireless networks. Proceedings of the 10th IEEE International Conference on Network Protocols, Nov 12-15, 2002, Paris, France. Piscataway, NJ, USA: IEEE, 2002: 216-225

12. Geurts P, Khayat I E, Leduc G. A machine learning approach to improve congestion control over wireless computer networks. Proceedings of the 4th IEEE International Conference on Data Mining (ICDM’04), Nov 1-4, 2004, Bringhton, UK. Piscataway, NJ, USA: IEEE, 2004: 383-386

13. Burges C J C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2(2): 1-43

[1]	Kang Xiaofei, Wang Tian, Liang Xian. Intelligent reflecting surfaces-assisted millimeter wave communication: Channel estimation based on deep learning[J]. 中国邮电高校学报(英文版), 2024, 31(1): 49-56.
[2]	吴岳, 陈向勇, 邱建龙, 胡顺伟, 赵峰. Dynamic event-triggered leader-follower consensus of nonlinear multi-agent systems under directed weighted topology [J]. 中国邮电高校学报(英文版), 2023, 30(6): 3-10.
[3]	吕鹏超, 黄俊杰, 刘波. Linear-quadratic optimal control for time-varying descriptor systems via space decompositions [J]. 中国邮电高校学报(英文版), 2023, 30(6): 38-48.
[4]	程尊水, 姜景娜, 孙东晟. Stability and Hopf bifurcation analysis in DCTCP congestion control[J]. 中国邮电高校学报(英文版), 2023, 30(6): 30-37.
[5]	彭宏刘耀宗. Joint global constraint and Fisher discrimination based multi-layer dictionary learning for image classification[J]. 中国邮电高校学报(英文版), 2023, 30(5): 1-10.
[6]	孟慧任利娜赵宗渠. Identity-based proxy re-encryption scheme from RLWE assumption with ciphertext evolution[J]. 中国邮电高校学报(英文版), 2023, 30(5): 51-60.
[7]	Feng Yuting, Gong Haobing, Hao Xiaojing, Gao Hui, Guo Xiangming. Machine-learning based ocean atmospheric duct forecasting: a hybrid model-data-driven approach[J]. 中国邮电高校学报(英文版), 2023, 30(4): 1-9.
[8]	Xu Ming, Dong Chen, Li Hanlu, Luo Qiming, Wang Lizi, Zhang Wanru, Liu Huixin. Design and implementation of unmanned aerial vehicle localization and light tracking system[J]. 中国邮电高校学报(英文版), 2023, 30(4): 67-74.
[9]	Li Hao, Zhang Linghua, Tong Cheng, Zhou Chenyang. Short-term load forecasting model based on gated recurrent unit and multi-head attention [J]. 中国邮电高校学报(英文版), 2023, 30(3): 25-31.
[10]	王丹志向键鑫崔岩松. KG-based memory recommendation algorithm for learning path[J]. 中国邮电高校学报(英文版), 2023, 30(2): 36-48.
[11]	吴青王凡范九伦侯静. L2,1-norm robust regularized extreme learning machine for regression using CCCP method [J]. 中国邮电高校学报(英文版), 2023, 30(2): 61-72.
[12]	Wu Qing, Li Feiyan, Zhang Hengchang, Fan Jiulun, Gao Xiaofeng. Least squares twin support vector machine with asymmetric squared loss[J]. 中国邮电高校学报(英文版), 2023, 30(1): 1-16.
[13]	Guo Xiangbo, Wang Jian, Huang Mengjie, Wang Minghui, Yang Jian, Yu Yongtao. Deep knowledge tracking algorithm based on forgetting law[J]. 中国邮电高校学报(英文版), 2023, 30(1): 17-27.
[14]	Wang Xianlun, Wang Guangyu, Cui Yuxia. Facial expression recognition based on improved ResNet[J]. 中国邮电高校学报(英文版), 2023, 30(1): 28-38.
[15]	Xu Siyang, Song Xin, Zhu Jiahui. Resource allocation optimization for SWIPT full-duplex relaying networks with practical energy harvester[J]. 中国邮电高校学报(英文版), 2023, 30(1): 39-46.

SVM-based loss differentiation algorithm for wir ed-cum-wireless networks

SVM-based loss differentiation algorithm for wir ed-cum-wireless networks

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价