Authors:Jingyuan Wang (wangjingyuan06@mails.tsinghua.edu.cn), Jiangtao Wen (jtwen@tsinghua.edu.cn), Jun Zhang, Yuxing Han (ericahan@ee.ucla.edu) Papers:Jingyuan Wang, Jiangtao Wen, Jun Zhang and Yuxing Han, "TCP-FIT - an improved TCP congestion control algorithm and its performance", Proc. 2011 IEEE Infocom, Shanghai, October, 2011. Jingyuan Wang, Jiangtao Wen, Jun Zhang, Yuxing Han, "TCP-FIT - a novel TCP congestion control algorithm for wireless networks", IEEE Globecome 2010 Workshop on Advances in Communications and Networks, December, 2010, Miami, FL. Jingyuan Wang, Jiangtao Wen, Jun Zhang, Yuxing Han, "A demonstration of a new TCP congestion control algorithm over LTE and other challenging networks," MobiCom 2010 Demo, Sept. Chicago, IL. (PDF) |
IntroductionTCP-FIT is a novel congestion control algorithm targeting both emerging wireless networks such as LTE, WiMax, Wi-Fi and HSPA and high speed long delay (high BDP) networks. The Transmission Control Protocol (TCP) is a reliable transport layer protocol that is widely used on the Internet. Congestion control algorithm is an integral module of TCP that directly determines the performance of the protocol. Standard congestion control algorithms such as TCP-Reno and TCP-NewReno, achieved great success for several decades but are found to perform poorly over wireless and/or high Bandwidth Delay Product (BDP) links. To improve TCP performance over wireless and high BDP networks, many TCP variants have been proposed, including TCP Westwood, TCP Veno for wireless applications and Compound TCP, TCP CUBIC, FAST TCP for high BDP networks. Although these algorithms have achieved success in their respective target applications, designing a TCP congestion control algorithm that perform gracefully in both wireless and high BDP networks is still a great challenge. On the other hand however, with the deployment of wireless networks, such as LTE, WiMAX, as well as high bandwidth, real time applications such as multimedia over TCP/HTTP, it is required for the TCP congestion algorithm to handle both wireless connections with random radio related losses as well as congestion-introduced issues typical for wired high BDP networks. To address these problems, a novel TCP congestion control algorithm for the heterogeneous networks which contained high BDP links and wireless links, named TCP-FIT, is proposed. A demo comparison between TCP-FIT and existing TCP algorithms over an wireless network is shown in the following video. As can be seen in fig. 1 to fig. 8, given the same challenging network conditions, TCP-FIT achieves significant performance improvement as compared with other algorithms. |
 |
 |
|
Fig.1 Average throughput of different TCP algorithms over CHINA TELECOM CDMA 2000 3G network |
Fig.2 Throughput variation of of different TCP algorithms over CHINA TELECOM CDMA 2000 3G network in 2 hours |
 |
 |
|
Fig.3 Average throughput of different TCP algorithms over WIFI network in Tsinghua University |
Fig.4 Throughput variation of different TCP algorithms over WIFI network in Tsinghua University in 2 hours |
 |
 |
|
Fig.5 Average throughput of different TCP algorithms over WIFI network at a Starbucks coffee shop in Beijing |
Fig.6Throughput variation of different TCP algorithms over WIFI network at a Starbucks coffee shop in Beijing in 2 hours |
Fig.7 Average throughput of different TCP algorithms over LTE network |
Fig.8 Throughput comparison between different TCP algorithms for varied LTE UE SINR. |
To test the performance of TCP-FIT over live,
deployed, real-world networks, we put a TCP-FIT server,
which located Orange, California, US, on the Internet and
tested TCP-FIT using clients located in 5 different cities/towns
in 4 countries (China, Switzerland, USA and India) on 3
continents. At each location, we tested a combination of wired
line connections, Wi-Fi and whenever possible, 3G wireless
networks. The location, network and OS of the test clients
are listed in Table IV. In each experiment, a script was used
to automatically and periodically cycle through different TCP
algorithms on the server over long durations of time (4-24
hours), while the client collected throughput information and
other useful data. The period for changing the TCP algorithms
was set to about 5-10 minutes, so that 1) the algorithms tested
were able to reach close to steady-state performances; 2) the
period is consistent with the durations of a reasonably large
percentage of the TCP based sessions on the Internet (e.g.
YouTube streaming of a single piece of content, synchronizing
emails, refreshing one web page, etc.). In the performance
measure, TCP-FIT are compared with CUBIC, CTCP, Reno in
all cases. HSTCP and Illinois are compared for wired network
and TCPW, Veno for wireless. The location, network and OS of the test clients
are listed in following table: |
|
| Our results are summarized in Following Figures: |
 |
 |
|
Figure 9. Wired Network in Zurich |
Figure 10. Wired Network in Los Angeles |
|
 |
 |
|
Figure 11. Wired Network in Beijing |
|
Figure 12. Wireless Network in Zurich |
 |
 |
|
Figure 13. Wireless Network in Los Angeles |
|
Figure 14. Wireless Network in Beijing |
 |
 |
|
Figure 15. 3G Network in Beijing |
|
Figure 16. 3G Network in a town of Fujian Province |
 |
||
Figure 17. ADSL Network in Bangalore |
we compared the performance of TCP-FIT with widely deployed TCP variants, TCP Reno and
CUBIC, using the Planet-Lab testbed. In the experiments, 245 nodes of the Planet-Lab test bed distributed
over 192 cities in 43 countries were used to download video clips from an HTTP servers located in San
Diego, CA. The geographical distribution of these nodes is shown in Figure 18. These nodes covered 233 ISPs representative of the conditions of the current Internet. |
 |
Figure 18. Geographical Distribution of Nodes in Planet-lab Experiments |
The cumulative probabilistic function for
the download throughputs of the nodes is shown in Figure 19, while the corresponding cumulative
probabilistic function for the TCP-FIT to Reno/CUBIC speedup is in Figure 20. |
 |
 |
|
| Figure. 19 Cumulative Probabilistic Function for Throughputs |
Figure. 20 Cumulative Probabilistic Function for Speedup |
 |
Lab of Multimedia and Networking Mail: tcpfit.thu@gmail.com Address: Room 1-512, FIT Building , Tsinghua University, Beijing 100084, P.R.China |
