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Title: TCon: A transparent congestion control deployment platform for optimizing WAN transfers
Authors: Zhang, Yuxiang
Cui, Lin
Tso, Fung Po
Guan, Quanlong
Jia, Weijia
Keywords: Congestion control
BBR
Transparent
Issue Date: 2017
Publisher: Springer © IFIP International Federation for Information Processing
Citation: ZHANG, Y. ... et al, 2017. TCon: A transparent congestion control deployment platform for optimizing WAN transfers. IN: Shi X. ... et al (eds). Network and Parallel Computing. NPC 2017. Lecture Notes in Computer Science, vol 10578. Cham: Springer, pp.49-61.
Series/Report no.: Lecture Notes in Computer Science;10578
Abstract: 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.
Description: This is a pre-copyedited version of a contribution published in Shi X. ... et al (eds). Network and Parallel Computing published by Springer. The definitive authenticated version is available online via http://dx.doi.org/10.1007/978-3-319-68210-5_5. This paper was also presented at the IFIP International Conference on Network and Parallel Computing (NPC 2017), Hefei, China, 20th-21st October 2017.
Sponsor: 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).
Version: Accepted for publication
DOI: 10.1007/978-3-319-68210-5_5
URI: https://dspace.lboro.ac.uk/2134/33376
Publisher Link: https://doi.org/10.1007/978-3-319-68210-5_5
ISBN: 9783319682099
ISSN: 0302-9743
Appears in Collections:Conference Papers and Presentations (Computer Science)

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