云网络故障诊断系统及工具综述

戚依宁; 方崇荣; 刘昊俣; 康达祥; 吕彪; 程鹏; 陈积明

doi:10.1631/FITEE.2000153

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云网络故障诊断系统及工具综述

常规文章 | Updated：2022-05-19

- 云网络故障诊断系统及工具综述
- A survey of cloud network fault diagnostic systems and tools
- 信息与电子工程前沿（英文） 2021年22卷第8期页码：1031-1045
- Affiliations：
  
  State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
  Alibaba Group, Hangzhou 310024, China
- Author bio：
  
  [ "", "Yining QI received her BS degree in Control Science and Engineering at Zhejiang University, Hangzhou, China, in 2019. She is currently an MS candidate in Control Science and Engineering at Zhejiang University. Her research interests are cloud network, network diagnostics, and network measurement. E-mail: qyning710@gmail.com" ]
  [ "Chongrong FANG, E-mail: chongrongfang.zju@gmail.com" ]
  [ "Haoyu LIU, E-mail: haoyu_liu@zju.edu.cn" ]
  [ "Daxiang KANG, E-mail: daxiang.kdx@alibaba-inc.com" ]
  [ "Biao LYU, E-mail: lubiao.lb@alibaba-inc.com" ]
  [ "", "Peng CHENG received his BS degree in Automation and his PhD degree in Control Science and Engineering at Zhejiang University, in 2004 and 2009, respectively. From 2012 to 2013, he worked as a research fellow in Information System Technology and Design Pillar at Singapore University of Technology and Design, Singapore. He is currently a professor at the College of Control Science and Engineering, Zhejiang University. He is a corresponding expert of Front Inform Technol Electron Eng. His research interests include networked sensing and control, cyber-physical systems, and control system security" ]
  [ "", "Jiming CHEN received his BS and PhD degrees in Control Science and Engineering at Zhejiang University, in 2000 and 2005, respectively. He was a visiting researcher at the University of Waterloo, Canada, from 2008 to 2010. He is the Deputy Director of the State Key Laboratory of Industrial Control Technology and a member of the Academic Committee at Zhejiang University. He is now serving as an editor of Front Inform Technol Electron Eng. His research interests include the Internet of Things, sensor networks, networked control, and control system security. He was a recipient of the Fok Ying Tung Young Teacher Award of the Ministry of Education and the IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award. He is an IEEE Vehicular Technology Society distinguished lecturer and an IEEE fellow. Jiming CHEN, E-mail: cjm@zju.edu.cn" ]
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61761136012 and 61833015), the Alibaba Innovative Research Program, China, and the Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies, China
- DOI：10.1631/FITEE.2000153
  中图分类号： TP306
- 收稿：2020-04-06，
  
  修回：2021-;5-;7，
  
  纸质出版：2021-08
- Accepted：
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戚依宁, 方崇荣, 刘昊俣, 等. 云网络故障诊断系统及工具综述[J]. 信息与电子工程前沿（英文）, 2021,22(8):1031-1045.

Yining QI, Chongrong FANG, Haoyu LIU, et al. A survey of cloud network fault diagnostic systems and tools[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(8): 1031-1045.
戚依宁, 方崇荣, 刘昊俣, 等. 云网络故障诊断系统及工具综述[J]. 信息与电子工程前沿（英文）, 2021,22(8):1031-1045. DOI： 10.1631/FITEE.2000153.

Yining QI, Chongrong FANG, Haoyu LIU, et al. A survey of cloud network fault diagnostic systems and tools[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(8): 1031-1045. DOI： 10.1631/FITEE.2000153.

摘要

近年来，云网络已成为支撑人们正常生产生活的重要基础产业。然而，随着云网络日益复杂化，网络故障越来越容易出现，并且造成巨大经济损失。因此，为保障云网络性能，防止故障造成恶劣影响，云网络故障诊断已成为云服务提供商的重点研究技术之一。由于云网络的特性（例如虚拟化和多租户），将传统网络诊断工具移植到云网络面临不少困难。此外，许多现有工具无法解决云网络的独有问题。本文总结了近年提出的可用于云网络生产环境的最先进的云网络故障诊断系统及工具，并根据其特点分类。此外，根据云网络特点，分析了云网络故障诊断与传统网络故障诊断的区别。考虑到云网络的实际生产需求，提出设计云网络故障诊断工具时应注意的要点。此外，讨论了云网络故障诊断在未来发展中面临的机遇与挑战。

Abstract

Recently

cloud computing has become a vital part that supports people's normal lives and production. However

accompanied by the increasing complexity of the cloud network

failures constantly keep coming up and cause huge economic losses. Thus

to guarantee the cloud network performance and prevent execrable effects caused by failures

cloud network diagnostics has become of great interest for cloud service providers. Due to the characteristics of cloud network (e.g.

virtualization and multi-tenancy)

transplanting traditional network diagnostic tools to the cloud network face several difficulties. Additionally

many existing tools cannot solve problems in the cloud network. In this paper

we summarize and classify the state-of-the-art technologies of cloud diagnostics which can be used in the production cloud network according to their features. Moreover

we analyze the differences between cloud network diagnostics and traditional network diagnostics based on the characteristics of the cloud network. Considering the operation requirements of the cloud network

we propose the points that should be cared about when designing a cloud network diagnostic tool. Also

we discuss the challenges that cloud network diagnostics will face in future development.

关键词

Keywords

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