TCon: A Transparent Congestion Control Deployment Platform for Optimizing WAN Transfers

  • Yuxiang Zhang
  • Lin CuiEmail author
  • Fung Po Tso
  • Quanlong Guan
  • Weijia Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10578)


Nowadays, many web services (e.g., cloud storage) are deployed inside datacenters and may trigger transfers to clients through WAN. TCP congestion control is a vital component for improving the performance (e.g., latency) of these services. Considering complex networking environment, the default congestion control algorithms on servers may not always be the most efficient, and new advanced algorithms will be proposed. However, adjusting congestion control algorithm usually requires modification of TCP stacks of servers, which is difficult if not impossible, especially considering different operating systems and configurations on servers. In this paper, we propose TCon, a light-weight, flexible and scalable platform that allows administrators (or operators) to deploy any appropriate congestion control algorithms transparently without making any changes to TCP stacks of servers. We have implemented TCon in Open vSwitch (OVS) and conducted extensive test-bed experiments by transparently deploying BBR congestion control algorithm over TCon. Test-bed results show that the BBR over TCon works effectively and the performance stays close to its native implementation on servers, reducing latency by 12.76% on average.


Congestion control BBR Transparent 



This work is partially supported by Chinese National Research Fund (NSFC) No. 61402200; NSFC Key Project No. 61532013; NSFC Project No. 61602210; National China 973 Project No. 2015CB352401; the UK Engineering and Physical Sciences Research Council (EPSRC) grants EP/P004407/1 and EP/P004024/1; Shanghai Scientific Innovation Act of STCSM No.15JC1402400 and 985 Project of SJTU with No. WF220103001; the Science and Technology Planning Project of Guangdong Province, China (2014A040401027, 2015A030401043), the Fundamental Research Funds for the Central Universities (21617409, 21617408); the Opening Project of Guangdong Province Key Laboratory of Big Data Analysis and Processing (2017009).


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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Yuxiang Zhang
    • 1
  • Lin Cui
    • 1
    • 4
    Email author
  • Fung Po Tso
    • 2
  • Quanlong Guan
    • 1
  • Weijia Jia
    • 3
  1. 1.Department of Computer ScienceJinan UniversityGuangzhouPeople’s Republic of China
  2. 2.Department of Computer ScienceLoughborough UniversityLoughboroughUK
  3. 3.Department of Computer Science and EngineeringSJTUShanghaiPeople’s Republic of China
  4. 4.Guangdong Key Laboratory of Big Data Analysis and ProcessingGuangzhouPeople’s Republic of China

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