When network slicing meets deep reinforcement learning

Qiang Liu; Tao Han

doi:10.1145/3360468.3366778

When network slicing meets deep reinforcement learning

Qiang Liu, Tao Han

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Scopus citations

Abstract

5G will serve various new use cases that have diverse requirements of multiple resources, e.g., radio, transportation, and computing. Network slicing is a promising technology to slice the network according to the requirements of different use cases. In this work, we present an end-to-end network slicing system that leverages deep reinforcement learning to efficiently orchestrate multiple resources in radio access network, transportation network, and edge computing servers to network slices.

Original language	English (US)
Title of host publication	CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019
Publisher	Association for Computing Machinery, Inc
Pages	29-30
Number of pages	2
ISBN (Electronic)	9781450370066
DOIs	https://doi.org/10.1145/3360468.3366778
State	Published - Dec 9 2019
Externally published	Yes
Event	15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019 - Orlando, United States Duration: Dec 9 2019 → Dec 12 2019

Publication series

Name	CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019

Conference

Conference	15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019
Country/Territory	United States
City	Orlando
Period	12/9/19 → 12/12/19

All Science Journal Classification (ASJC) codes

Hardware and Architecture
Electrical and Electronic Engineering
Computer Networks and Communications

Keywords

Deep Reinforcement Learning
Network Slicing

Access to Document

10.1145/3360468.3366778

Cite this

Liu, Q., & Han, T. (2019). When network slicing meets deep reinforcement learning. In CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019 (pp. 29-30). (CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3360468.3366778

Liu, Qiang ; Han, Tao. / When network slicing meets deep reinforcement learning. CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019. Association for Computing Machinery, Inc, 2019. pp. 29-30 (CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019).

@inproceedings{8340ae76e9704f7ea033687e384440ff,

title = "When network slicing meets deep reinforcement learning",

abstract = "5G will serve various new use cases that have diverse requirements of multiple resources, e.g., radio, transportation, and computing. Network slicing is a promising technology to slice the network according to the requirements of different use cases. In this work, we present an end-to-end network slicing system that leverages deep reinforcement learning to efficiently orchestrate multiple resources in radio access network, transportation network, and edge computing servers to network slices.",

keywords = "Deep Reinforcement Learning, Network Slicing",

author = "Qiang Liu and Tao Han",

note = "Publisher Copyright: {\textcopyright} 2019 held by the owner/author(s).; 15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

year = "2019",

month = dec,

day = "9",

doi = "10.1145/3360468.3366778",

language = "English (US)",

series = "CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019",

publisher = "Association for Computing Machinery, Inc",

pages = "29--30",

booktitle = "CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019",

}

Liu, Q & Han, T 2019, When network slicing meets deep reinforcement learning. in CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019. CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019, Association for Computing Machinery, Inc, pp. 29-30, 15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019, Orlando, United States, 12/9/19. https://doi.org/10.1145/3360468.3366778

When network slicing meets deep reinforcement learning. / Liu, Qiang; Han, Tao.
CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019. Association for Computing Machinery, Inc, 2019. p. 29-30 (CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - When network slicing meets deep reinforcement learning

AU - Liu, Qiang

AU - Han, Tao

PY - 2019/12/9

Y1 - 2019/12/9

N2 - 5G will serve various new use cases that have diverse requirements of multiple resources, e.g., radio, transportation, and computing. Network slicing is a promising technology to slice the network according to the requirements of different use cases. In this work, we present an end-to-end network slicing system that leverages deep reinforcement learning to efficiently orchestrate multiple resources in radio access network, transportation network, and edge computing servers to network slices.

AB - 5G will serve various new use cases that have diverse requirements of multiple resources, e.g., radio, transportation, and computing. Network slicing is a promising technology to slice the network according to the requirements of different use cases. In this work, we present an end-to-end network slicing system that leverages deep reinforcement learning to efficiently orchestrate multiple resources in radio access network, transportation network, and edge computing servers to network slices.

KW - Deep Reinforcement Learning

KW - Network Slicing

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M3 - Conference contribution

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PB - Association for Computing Machinery, Inc

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Y2 - 9 December 2019 through 12 December 2019

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Liu Q, Han T. When network slicing meets deep reinforcement learning. In CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019. Association for Computing Machinery, Inc. 2019. p. 29-30. (CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019). doi: 10.1145/3360468.3366778

When network slicing meets deep reinforcement learning

Abstract

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All Science Journal Classification (ASJC) codes

Keywords

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