%0 Conference Proceedings
%T TCon: A Transparent Congestion Control Deployment Platform for Optimizing WAN Transfers
%+ Jinan University [Guangzhou]
%+ Key Laboratory of Big Data Analysis and Processing
%+ Loughborough University
%+ Shandong Jiaotong University [Jinan]
%+ School of Physics and Astronomy and SJTU-ParisTech Elite
%A Zhang, Yuxiang
%A Cui, Lin
%A Tso, Fung, Po
%A Guan, Quanlong
%A Jia, Weijia
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 14th IFIP International Conference on Network and Parallel Computing (NPC)
%C Hefei, China
%Y Xuanhua Shi
%Y Hong An
%Y Chao Wang
%Y Mahmut Kandemir
%Y Hai Jin
%I Springer International Publishing
%3 Network and Parallel Computing
%V LNCS-10578
%P 49-61
%8 2017-10-20
%D 2017
%R 10.1007/978-3-319-68210-5_5
%K Congestion control
%K BBR
%K Transparent
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-01705451/document
%2 https://inria.hal.science/hal-01705451/file/457609_1_En_5_Chapter.pdf
%L hal-01705451
%U https://inria.hal.science/hal-01705451
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-10578