Service capacity enhanced task offloading and resource allocation in multi-server edge computing environment

Wei Du; Tao Lei; Qiang Het; Wei Liu; Qiwang Lei; Hailiang Zhao; Wei Wang

doi:10.1109/ICWS.2019.00025

Service capacity enhanced task offloading and resource allocation in multi-server edge computing environment

Wei Du
, Tao Lei
, Qiang Het
, Wei Liu
, Qiwang Lei
, Hailiang Zhao
, Wei Wang

School of Data Science and Engineering

Wuhan University of Technology
Hubei Key Laboratory of Transportation Internet of Things
Swinburne University of Technology

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

24 Scopus citations

Abstract

An edge computing environment features multiple edge servers and multiple service clients. In this environment, mobile service providers can offload client-side computation tasks from service clients' devices onto edge servers to reduce service latency and power consumption experienced by the clients. A critical issue that has yet to be properly addressed is how to allocate edge computing resources to achieve two optimization objectives: 1) minimize the service cost measured by the service latency and the power consumption experienced by service clients; and 2) maximize the service capacity measured by the number of service clients that can offload their computation tasks in the long term. This paper formulates this long-term problem as a stochastic optimization problem and solves it with an online algorithm based on Lyapunov optimization. This NPhard problem is decomposed into three sub-problems, which are then solved with a suite of techniques. The experimental results show that our approach significantly outperforms two baseline approaches.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services
Editors	Elisa Bertino, Carl K. Chang, Peter Chen, Ernesto Damiani, Ernesto Damiani, Michael Goul, Katsunori Oyama
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	83-90
Number of pages	8
ISBN (Electronic)	9781728127170
DOIs	https://doi.org/10.1109/ICWS.2019.00025
State	Published - Jul 2019
Event	26th IEEE International Conference on Web Services, ICWS 2019 - Milan, Italy Duration: 8 Jul 2019 → 13 Jul 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services

Conference

Conference	26th IEEE International Conference on Web Services, ICWS 2019
Country/Territory	Italy
City	Milan
Period	8/07/19 → 13/07/19

Keywords

Edge computing
Lyapunov optimization
Multi-server task offloading
Service capacity enhancement

Access to Document

10.1109/ICWS.2019.00025

Cite this

Du, W., Lei, T., Het, Q., Liu, W., Lei, Q., Zhao, H., & Wang, W. (2019). Service capacity enhanced task offloading and resource allocation in multi-server edge computing environment. In E. Bertino, C. K. Chang, P. Chen, E. Damiani, E. Damiani, M. Goul, & K. Oyama (Eds.), Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services (pp. 83-90). Article 8818444 (Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICWS.2019.00025

Du, Wei ; Lei, Tao ; Het, Qiang et al. / Service capacity enhanced task offloading and resource allocation in multi-server edge computing environment. Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services. editor / Elisa Bertino ; Carl K. Chang ; Peter Chen ; Ernesto Damiani ; Ernesto Damiani ; Michael Goul ; Katsunori Oyama. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 83-90 (Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services).

@inproceedings{8eee4ee83add42258b85a3487135260a,

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abstract = "An edge computing environment features multiple edge servers and multiple service clients. In this environment, mobile service providers can offload client-side computation tasks from service clients' devices onto edge servers to reduce service latency and power consumption experienced by the clients. A critical issue that has yet to be properly addressed is how to allocate edge computing resources to achieve two optimization objectives: 1) minimize the service cost measured by the service latency and the power consumption experienced by service clients; and 2) maximize the service capacity measured by the number of service clients that can offload their computation tasks in the long term. This paper formulates this long-term problem as a stochastic optimization problem and solves it with an online algorithm based on Lyapunov optimization. This NPhard problem is decomposed into three sub-problems, which are then solved with a suite of techniques. The experimental results show that our approach significantly outperforms two baseline approaches.",

keywords = "Edge computing, Lyapunov optimization, Multi-server task offloading, Service capacity enhancement",

author = "Wei Du and Tao Lei and Qiang Het and Wei Liu and Qiwang Lei and Hailiang Zhao and Wei Wang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 26th IEEE International Conference on Web Services, ICWS 2019 ; Conference date: 08-07-2019 Through 13-07-2019",

year = "2019",

month = jul,

doi = "10.1109/ICWS.2019.00025",

language = "英语",

series = "Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services",

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

pages = "83--90",

editor = "Elisa Bertino and Chang, \{Carl K.\} and Peter Chen and Ernesto Damiani and Ernesto Damiani and Michael Goul and Katsunori Oyama",

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Du, W, Lei, T, Het, Q, Liu, W, Lei, Q, Zhao, H & Wang, W 2019, Service capacity enhanced task offloading and resource allocation in multi-server edge computing environment. in E Bertino, CK Chang, P Chen, E Damiani, E Damiani, M Goul & K Oyama (eds), Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services., 8818444, Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services, Institute of Electrical and Electronics Engineers Inc., pp. 83-90, 26th IEEE International Conference on Web Services, ICWS 2019, Milan, Italy, 8/07/19. https://doi.org/10.1109/ICWS.2019.00025

Service capacity enhanced task offloading and resource allocation in multi-server edge computing environment. / Du, Wei; Lei, Tao; Het, Qiang et al.
Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services. ed. / Elisa Bertino; Carl K. Chang; Peter Chen; Ernesto Damiani; Ernesto Damiani; Michael Goul; Katsunori Oyama. Institute of Electrical and Electronics Engineers Inc., 2019. p. 83-90 8818444 (Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services).

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

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AU - Du, Wei

AU - Lei, Tao

AU - Het, Qiang

AU - Liu, Wei

AU - Lei, Qiwang

AU - Zhao, Hailiang

AU - Wang, Wei

PY - 2019/7

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N2 - An edge computing environment features multiple edge servers and multiple service clients. In this environment, mobile service providers can offload client-side computation tasks from service clients' devices onto edge servers to reduce service latency and power consumption experienced by the clients. A critical issue that has yet to be properly addressed is how to allocate edge computing resources to achieve two optimization objectives: 1) minimize the service cost measured by the service latency and the power consumption experienced by service clients; and 2) maximize the service capacity measured by the number of service clients that can offload their computation tasks in the long term. This paper formulates this long-term problem as a stochastic optimization problem and solves it with an online algorithm based on Lyapunov optimization. This NPhard problem is decomposed into three sub-problems, which are then solved with a suite of techniques. The experimental results show that our approach significantly outperforms two baseline approaches.

AB - An edge computing environment features multiple edge servers and multiple service clients. In this environment, mobile service providers can offload client-side computation tasks from service clients' devices onto edge servers to reduce service latency and power consumption experienced by the clients. A critical issue that has yet to be properly addressed is how to allocate edge computing resources to achieve two optimization objectives: 1) minimize the service cost measured by the service latency and the power consumption experienced by service clients; and 2) maximize the service capacity measured by the number of service clients that can offload their computation tasks in the long term. This paper formulates this long-term problem as a stochastic optimization problem and solves it with an online algorithm based on Lyapunov optimization. This NPhard problem is decomposed into three sub-problems, which are then solved with a suite of techniques. The experimental results show that our approach significantly outperforms two baseline approaches.

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BT - Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services

A2 - Bertino, Elisa

A2 - Chang, Carl K.

A2 - Chen, Peter

A2 - Damiani, Ernesto

A2 - Goul, Michael

A2 - Oyama, Katsunori

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th IEEE International Conference on Web Services, ICWS 2019

Y2 - 8 July 2019 through 13 July 2019

ER -

Du W, Lei T, Het Q, Liu W, Lei Q, Zhao H et al. Service capacity enhanced task offloading and resource allocation in multi-server edge computing environment. In Bertino E, Chang CK, Chen P, Damiani E, Damiani E, Goul M, Oyama K, editors, Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services. Institute of Electrical and Electronics Engineers Inc. 2019. p. 83-90. 8818444. (Proceedings - 2019 IEEE International Conference on Web Services, ICWS 2019 - Part of the 2019 IEEE World Congress on Services). doi: 10.1109/ICWS.2019.00025

Service capacity enhanced task offloading and resource allocation in multi-server edge computing environment

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Keywords

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