Traffic Control for RDMA-Enabled Data Center Networks: A Survey

Zehua Guo, Sen Liu, Zhi Li Zhang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Data centers, the infrastructure of cloud computing, have been widely deployed around the world to accommodate the increasing cloud computing demands. A data center network (DCN) connects tens or hundreds of thousands of servers in the data center and uses a traffic control scheme to enable the data transmission among servers. Various new applications in cloud present new requirements on traffic control of DCNs, such as low latency and high throughput. Existing traffic control schemes in DCNs suffer from the complicated kernel processing and cannot satisfy the requirements. Remote direct memory access (RDMA), which bypasses the kernel processing to enable fast memory moving across a network, is recognized as a promising solution. In this article, we present a survey of traffic control schemes for traditional RDMA, traditional DCNs, and RDMA-enabled DCNs and explain their limitations. We also differentiate the existing schemes from congestion control, performance, and components. In order to encourage future research, we point out some potential research directions of this research.

Original languageEnglish (US)
Article number8827710
Pages (from-to)677-688
Number of pages12
JournalIEEE Systems Journal
Volume14
Issue number1
DOIs
StatePublished - Mar 2020

Bibliographical note

Funding Information:
This work was supported in part by the National Science Foundation under Grants CNS-1618339, CNS-1617729, CNS-1814322, and CNS-1836772, in part by the National Key Research and Development Program of China under Grant 2018YFB1003700, in part by the National Natural Science Foundation of China under Grant 61836001, in part by the Natural Science Foundation of Beijing under Grant Z170003, in part by the China Scholarship Council under Grant 201706370143, and in part by the Beijing Institute of Technology Research Fund Program for Young Scholars

Funding Information:
Manuscript received November 6, 2018; revised January 14, 2019 and April 27, 2019; accepted June 1, 2019. Date of publication September 9, 2019; date of current version March 2, 2020. This work was supported in part by the National Science Foundation under Grants CNS-1618339, CNS-1617729, CNS-1814322, and CNS-1836772, in part by the National Key Research and Development Program of China under Grant 2018YFB1003700, in part by the National Natural Science Foundation of China under Grant 61836001, in part by the Natural Science Foundation of Beijing under Grant Z170003, in part by the China Scholarship Council under Grant 201706370143, and in part by the Beijing Institute of Technology Research Fund Program for Young Scholars. (Corresponding author: Sen Liu.) Z. Guo was with the Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455 USA. He is now with the School of Automation, Beijing Institute of Technology, Beijing 100081, China (e-mail: guo@bit.edu.cn).

Publisher Copyright:
© 2007-2012 IEEE.

Keywords

  • Congestion control
  • data center networks (DCNs)
  • remote direct memory access (RDMA)
  • survey
  • traffic control

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