Deep Reinforcement Learning for Router Selection in Network With Heavy Traffic

Bibliographic Details
Title:	Deep Reinforcement Learning for Router Selection in Network With Heavy Traffic
Authors:	Ruijin Ding, Yadong Xu, Feifei Gao, Xuemin Shen, Wen Wu
Source:	IEEE Access, Vol 7, Pp 37109-37120 (2019)
Publisher Information:	IEEE, 2019.
Publication Year:	2019
Collection:	LCC:Electrical engineering. Electronics. Nuclear engineering
Subject Terms:	Deep reinforcement learning, routing, network congestion, network throughput, deep Q network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
More Details:	The rapid development of wireless communications brings a tremendous increase in the amount number of data streams and poses significant challenges to the traditional routing protocols. In this paper, we leverage deep reinforcement learning (DRL) for router selection in the network with heavy traffic, aiming at reducing the network congestion and the length of the data transmission path. We first illustrate the challenges of the existing routing protocols when the amount of the data explodes. We then utilize the Markov decision process (RSMDP) to formulate the routing problem. Two novel deep Q network (DQN)-based algorithms are designed to reduce the network congestion probability with a short transmission path: one focusing on reducing the congestion probability; while the other focuses on shortening the transmission path. The simulation results demonstrate that the proposed algorithms can achieve higher network throughput comparing to existing routing algorithms in heavy network traffic scenarios.
Document Type:	article
File Description:	electronic resource
Language:	English
ISSN:	2169-3536
Relation:	https://ieeexplore.ieee.org/document/8673947/; https://doaj.org/toc/2169-3536
DOI:	10.1109/ACCESS.2019.2904539
Access URL:	https://doaj.org/article/5acc798d4a2b4d828859c18801026f36
Accession Number:	edsdoj.5acc798d4a2b4d828859c18801026f36
Database:	Directory of Open Access Journals

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Items	– Name: Title Label: Title Group: Ti Data: Deep Reinforcement Learning for Router Selection in Network With Heavy Traffic – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ruijin+Ding%22">Ruijin Ding</searchLink><br /><searchLink fieldCode="AR" term="%22Yadong+Xu%22">Yadong Xu</searchLink><br /><searchLink fieldCode="AR" term="%22Feifei+Gao%22">Feifei Gao</searchLink><br /><searchLink fieldCode="AR" term="%22Xuemin+Shen%22">Xuemin Shen</searchLink><br /><searchLink fieldCode="AR" term="%22Wen+Wu%22">Wen Wu</searchLink> – Name: TitleSource Label: Source Group: Src Data: IEEE Access, Vol 7, Pp 37109-37120 (2019) – Name: Publisher Label: Publisher Information Group: PubInfo Data: IEEE, 2019. – Name: DatePubCY Label: Publication Year Group: Date Data: 2019 – Name: Subset Label: Collection Group: HoldingsInfo Data: LCC:Electrical engineering. Electronics. Nuclear engineering – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+reinforcement+learning%22">Deep reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22routing%22">routing</searchLink><br /><searchLink fieldCode="DE" term="%22network+congestion%22">network congestion</searchLink><br /><searchLink fieldCode="DE" term="%22network+throughput%22">network throughput</searchLink><br /><searchLink fieldCode="DE" term="%22deep+Q+network%22">deep Q network</searchLink><br /><searchLink fieldCode="DE" term="%22Electrical+engineering%2E+Electronics%2E+Nuclear+engineering%22">Electrical engineering. Electronics. Nuclear engineering</searchLink><br /><searchLink fieldCode="DE" term="%22TK1-9971%22">TK1-9971</searchLink> – Name: Abstract Label: Description Group: Ab Data: The rapid development of wireless communications brings a tremendous increase in the amount number of data streams and poses significant challenges to the traditional routing protocols. In this paper, we leverage deep reinforcement learning (DRL) for router selection in the network with heavy traffic, aiming at reducing the network congestion and the length of the data transmission path. We first illustrate the challenges of the existing routing protocols when the amount of the data explodes. We then utilize the Markov decision process (RSMDP) to formulate the routing problem. Two novel deep Q network (DQN)-based algorithms are designed to reduce the network congestion probability with a short transmission path: one focusing on reducing the congestion probability; while the other focuses on shortening the transmission path. The simulation results demonstrate that the proposed algorithms can achieve higher network throughput comparing to existing routing algorithms in heavy network traffic scenarios. – Name: TypeDocument Label: Document Type Group: TypDoc Data: article – Name: Format Label: File Description Group: SrcInfo Data: electronic resource – Name: Language Label: Language Group: Lang Data: English – Name: ISSN Label: ISSN Group: ISSN Data: 2169-3536 – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://ieeexplore.ieee.org/document/8673947/; https://doaj.org/toc/2169-3536 – Name: DOI Label: DOI Group: ID Data: 10.1109/ACCESS.2019.2904539 – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://doaj.org/article/5acc798d4a2b4d828859c18801026f36" linkWindow="_blank">https://doaj.org/article/5acc798d4a2b4d828859c18801026f36</link> – Name: AN Label: Accession Number Group: ID Data: edsdoj.5acc798d4a2b4d828859c18801026f36
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RecordInfo	BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/ACCESS.2019.2904539 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 37109 Subjects: – SubjectFull: Deep reinforcement learning Type: general – SubjectFull: routing Type: general – SubjectFull: network congestion Type: general – SubjectFull: network throughput Type: general – SubjectFull: deep Q network Type: general – SubjectFull: Electrical engineering. Electronics. Nuclear engineering Type: general – SubjectFull: TK1-9971 Type: general Titles: – TitleFull: Deep Reinforcement Learning for Router Selection in Network With Heavy Traffic Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ruijin Ding – PersonEntity: Name: NameFull: Yadong Xu – PersonEntity: Name: NameFull: Feifei Gao – PersonEntity: Name: NameFull: Xuemin Shen – PersonEntity: Name: NameFull: Wen Wu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 21693536 Numbering: – Type: volume Value: 7 Titles: – TitleFull: IEEE Access Type: main
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