Learning Multi-paths for Edge Networks in a Stochastic Approximation Approach

Chengwei Zhang; Hanni Cheng; Xiaojun Hei; Brahim Bensaou

doi:10.1109/ICCE-China.2018.8448563

Learning Multi-paths for Edge Networks in a Stochastic Approximation Approach

Chengwei Zhang, Hanni Cheng, Xiaojun Hei^*, Brahim Bensaou

^*Corresponding author for this work

Department of Computer Science and Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

1 Citation (Scopus)

Abstract

Millions of edge devices are now equipped with increasingly strong computing, communication and storage capabilities. It is beneficial to connect these edge devices into networks for sharing different network service workloads so that these services are close to end-users and achieve reduced network access delay. In this paper, we proposed a measurement-assisted learning algorithm to find efficient multi paths between edge nodes with the assistance of intermediate nodes serving as an edge layer for reduced delay in edge networks in a stochastic approximation approach. Our simulation results demonstrate the effectiveness of the proposed learning algorithm.

Original language	English
Title of host publication	2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781538663011
DOIs	https://doi.org/10.1109/ICCE-China.2018.8448563
Publication status	Published - 27 Aug 2018
Event	5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018 - Taichung, Taiwan, Province of China Duration: 19 May 2018 → 21 May 2018

Publication series

Name	2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018

Conference

Conference	5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
Country/Territory	Taiwan, Province of China
City	Taichung
Period	19/05/18 → 21/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

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10.1109/ICCE-China.2018.8448563

Cite this

Zhang, C., Cheng, H., Hei, X., & Bensaou, B. (2018). Learning Multi-paths for Edge Networks in a Stochastic Approximation Approach. In 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018 Article 8448563 (2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-China.2018.8448563

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title = "Learning Multi-paths for Edge Networks in a Stochastic Approximation Approach",

abstract = "Millions of edge devices are now equipped with increasingly strong computing, communication and storage capabilities. It is beneficial to connect these edge devices into networks for sharing different network service workloads so that these services are close to end-users and achieve reduced network access delay. In this paper, we proposed a measurement-assisted learning algorithm to find efficient multi paths between edge nodes with the assistance of intermediate nodes serving as an edge layer for reduced delay in edge networks in a stochastic approximation approach. Our simulation results demonstrate the effectiveness of the proposed learning algorithm.",

author = "Chengwei Zhang and Hanni Cheng and Xiaojun Hei and Brahim Bensaou",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018 ; Conference date: 19-05-2018 Through 21-05-2018",

year = "2018",

month = aug,

day = "27",

doi = "10.1109/ICCE-China.2018.8448563",

language = "English",

isbn = "9781538663011",

series = "2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018",

}

Zhang, C, Cheng, H, Hei, X & Bensaou, B 2018, Learning Multi-paths for Edge Networks in a Stochastic Approximation Approach. in 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018., 8448563, 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018, Institute of Electrical and Electronics Engineers Inc., 5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018, Taichung, Taiwan, Province of China, 19/05/18. https://doi.org/10.1109/ICCE-China.2018.8448563

Learning Multi-paths for Edge Networks in a Stochastic Approximation Approach. / Zhang, Chengwei; Cheng, Hanni; Hei, Xiaojun et al.
2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8448563 (2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Learning Multi-paths for Edge Networks in a Stochastic Approximation Approach

AU - Zhang, Chengwei

AU - Cheng, Hanni

AU - Hei, Xiaojun

AU - Bensaou, Brahim

PY - 2018/8/27

Y1 - 2018/8/27

N2 - Millions of edge devices are now equipped with increasingly strong computing, communication and storage capabilities. It is beneficial to connect these edge devices into networks for sharing different network service workloads so that these services are close to end-users and achieve reduced network access delay. In this paper, we proposed a measurement-assisted learning algorithm to find efficient multi paths between edge nodes with the assistance of intermediate nodes serving as an edge layer for reduced delay in edge networks in a stochastic approximation approach. Our simulation results demonstrate the effectiveness of the proposed learning algorithm.

AB - Millions of edge devices are now equipped with increasingly strong computing, communication and storage capabilities. It is beneficial to connect these edge devices into networks for sharing different network service workloads so that these services are close to end-users and achieve reduced network access delay. In this paper, we proposed a measurement-assisted learning algorithm to find efficient multi paths between edge nodes with the assistance of intermediate nodes serving as an edge layer for reduced delay in edge networks in a stochastic approximation approach. Our simulation results demonstrate the effectiveness of the proposed learning algorithm.

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UR - https://www.scopus.com/pages/publications/85053867699

U2 - 10.1109/ICCE-China.2018.8448563

DO - 10.1109/ICCE-China.2018.8448563

M3 - Conference Paper published in a book

SN - 9781538663011

T3 - 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018

BT - 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018

Y2 - 19 May 2018 through 21 May 2018

ER -

Zhang C, Cheng H, Hei X, Bensaou B. Learning Multi-paths for Edge Networks in a Stochastic Approximation Approach. In 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8448563. (2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018). doi: 10.1109/ICCE-China.2018.8448563

Learning Multi-paths for Edge Networks in a Stochastic Approximation Approach

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