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author:

Chen, Xiang (Chen, Xiang.) [1] | Liu, Hongyan (Liu, Hongyan.) [2] | Huang, Qun (Huang, Qun.) [3] | Zhang, Dong (Zhang, Dong.) [4] (Scholars:张栋) | Zhou, Haifeng (Zhou, Haifeng.) [5] | Wu, Chunming (Wu, Chunming.) [6] | Liu, Xuan (Liu, Xuan.) [7] | Yang, Qiang (Yang, Qiang.) [8]

Indexed by:

EI Scopus SCIE

Abstract:

Programmable switches empower stateful packet processing, in which incoming packets continuously update states in the data plane, while applications in the control plane read and write states. However, since the data plane and control plane are separated, a consistent view of states in both planes is required for stateful packet processing. Existing approaches suffer from either high latency or low accuracy. In this paper, we propose, a framework that offers approximate state synchronization with low latency and high accuracy. To achieve low latency, directly transfers states between switch ASICs and the control plane by bypassing switch operating systems. To achieve high accuracy, utilizes the resources in the switch ASIC to realize rate control in state synchronization, such that it avoids potential state loss. It also bounds the divergence between the states in the data plane and that in the control plane under limited link capacity. We prototype on Barefoot Tofino switches. The experimental results indicate that compared to existing approaches, achieves order-of-magnitude latency reduction while maintaining high accuracy of state synchronization. Also, our experiments demonstrate that provides significant latency benefits to existing network management applications and well preserves high application-level accuracy.

Keyword:

approximate techniques Computer science Control systems Low latency communication programmable switches Protocols Registers Research and development State synchronization Synchronization

Community:

  • [ 1 ] [Chen, Xiang]Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310007, Peoples R China
  • [ 2 ] [Liu, Hongyan]Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310007, Peoples R China
  • [ 3 ] [Wu, Chunming]Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310007, Peoples R China
  • [ 4 ] [Huang, Qun]Peking Univ, Dept Comp Sci & Technol, Beijing 100871, Peoples R China
  • [ 5 ] [Zhang, Dong]Fuzhou Univ, Coll Comp Sci & Big Data, Fuzhou 350116, Peoples R China
  • [ 6 ] [Zhou, Haifeng]Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310007, Peoples R China
  • [ 7 ] [Liu, Xuan]Yangzhou Univ, Coll Informat Engn, Coll Artificial Intelligence, Yangzhou 225012, Peoples R China
  • [ 8 ] [Liu, Xuan]Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China
  • [ 9 ] [Yang, Qiang]Zhejiang Univ, Coll Elect Engn, Hangzhou 310007, Peoples R China

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Source :

IEEE-ACM TRANSACTIONS ON NETWORKING

ISSN: 1063-6692

Year: 2022

3 . 7

JCR@2022

3 . 7 0 0

JCR@2022

ESI Discipline: COMPUTER SCIENCE;

ESI HC Threshold:46

JCR Journal Grade:2

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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