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A Survey on Deep Learning for the Routing Layer of Computer Network

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

Abstract

With recent achievements in deep learning over the past year, many computer and network applications actively used deep learning architectures including convolution neural network and long short-term memory to improve the performance of their approach through them. The computer network used a complex and dynamic system. For example, routing is the main networking tasks in the fields of the communication network and it is widely used to optimize the optimal routing from the original host to the destination host. However, most of the traditional routing protocol is based on the experience of experts. In this paper, we present an overview of deep learning methods for the routing layer in the computer network. Furthermore, this paper discusses reinforcement learning methods about network routing. Finally, we outline a summary of the current state-of-the-art approaches along with some future research directions.

Original languageEnglish
Title of host publication2019 UK/China Emerging Technologies, UCET 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728127972
DOIs
StatePublished - Aug 2019
Externally publishedYes
Event2019 UK/China Emerging Technologies, UCET 2019 - Glasgow, Scotland, United Kingdom
Duration: 21 Aug 201922 Aug 2019

Publication series

Name2019 UK/China Emerging Technologies, UCET 2019

Conference

Conference2019 UK/China Emerging Technologies, UCET 2019
Country/TerritoryUnited Kingdom
CityGlasgow, Scotland
Period21/08/1922/08/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Computer network
  • Deep learning
  • Reinforcement learning
  • Routing layer

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Health Informatics
  • Instrumentation

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