Power Efficient User Cooperative Computation to Maximize Completed Tasks in MEC Networks

Yijin Pan; Cunhua Pan; Kezhi Wang; Huiling Zhu; Jiangzhou Wang

doi:10.1109/GLOBECOM38437.2019.9013629

Power Efficient User Cooperative Computation to Maximize Completed Tasks in MEC Networks

Yijin Pan, Cunhua Pan, Kezhi Wang, Huiling Zhu, Jiangzhou Wang

Computer and Information Sciences

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

3 Citations (Scopus)

12 Downloads (Pure)

Abstract

In this paper, the user cooperative task computation is explored by sharing the computing capability of the user equipments (UEs) so as to enhance the performance of mobile edge computing (MEC) networks. The number of completed tasks is maximized while minimizing the total power consumption of the UEs by jointly optimizing the user task offloading decision, the computational speed for the offloaded task and the transmit power for task offloading. An iterative algorithm based on the linear programming relaxation is proposed to solve the formulated mixed integer non-linear problem. The simulation results show that the proposed user cooperative computation scheme can achieve a higher completed tasks ratio than the noncooperative scheme.

Original language	English
Title of host publication	Proceedings of the 2019 2019 IEEE Global Communications Conference (GLOBECOM), December 9 – 13, in Waikoloa, Hawaii, USA
Place of Publication	Piscataway, NJ
Publisher	IEEE
ISBN (Electronic)	9781728109626
ISBN (Print)	9781728109633
DOIs	https://doi.org/10.1109/GLOBECOM38437.2019.9013629
Publication status	Published - Dec 2019
Event	2019 IEEE Global Communications Conference - Waikoloa, Big Island, Hawaii, United States Duration: 9 Dec 2019 → 13 Dec 2019 https://globecom2019.ieee-globecom.org/

Conference

Conference	2019 IEEE Global Communications Conference
Abbreviated title	GLOBECOM 2019
Country/Territory	United States
Period	9/12/19 → 13/12/19
Internet address	https://globecom2019.ieee-globecom.org/

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10.1109/GLOBECOM38437.2019.9013629

1570549211_Accepted author manuscript, 220 KB

Cite this

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title = "Power Efficient User Cooperative Computation to Maximize Completed Tasks in MEC Networks",

abstract = "In this paper, the user cooperative task computation is explored by sharing the computing capability of the user equipments (UEs) so as to enhance the performance of mobile edge computing (MEC) networks. The number of completed tasks is maximized while minimizing the total power consumption of the UEs by jointly optimizing the user task offloading decision, the computational speed for the offloaded task and the transmit power for task offloading. An iterative algorithm based on the linear programming relaxation is proposed to solve the formulated mixed integer non-linear problem. The simulation results show that the proposed user cooperative computation scheme can achieve a higher completed tasks ratio than the noncooperative scheme.",

keywords = "Task analysis, servers, power demand, device-to-device communication, Mobile handsets, computational modeling, edge computing",

author = "Yijin Pan and Cunhua Pan and Kezhi Wang and Huiling Zhu and Jiangzhou Wang",

year = "2019",

month = dec,

doi = "10.1109/GLOBECOM38437.2019.9013629",

language = "English",

isbn = "9781728109633",

booktitle = "Proceedings of the 2019 2019 IEEE Global Communications Conference (GLOBECOM), December 9 – 13, in Waikoloa, Hawaii, USA",

publisher = "IEEE",

address = "United States",

note = "2019 IEEE Global Communications Conference, GLOBECOM 2019 ; Conference date: 09-12-2019 Through 13-12-2019",

url = "https://globecom2019.ieee-globecom.org/",

}

Pan, Y, Pan, C, Wang, K, Zhu, H & Wang, J 2019, Power Efficient User Cooperative Computation to Maximize Completed Tasks in MEC Networks. in Proceedings of the 2019 2019 IEEE Global Communications Conference (GLOBECOM), December 9 – 13, in Waikoloa, Hawaii, USA ., 1570549211, IEEE, Piscataway, NJ, 2019 IEEE Global Communications Conference, United States, 9/12/19. https://doi.org/10.1109/GLOBECOM38437.2019.9013629

Power Efficient User Cooperative Computation to Maximize Completed Tasks in MEC Networks. / Pan, Yijin; Pan, Cunhua; Wang, Kezhi et al.

Proceedings of the 2019 2019 IEEE Global Communications Conference (GLOBECOM), December 9 – 13, in Waikoloa, Hawaii, USA . Piscataway, NJ : IEEE, 2019. 1570549211.

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

TY - GEN

T1 - Power Efficient User Cooperative Computation to Maximize Completed Tasks in MEC Networks

AU - Pan, Yijin

AU - Pan, Cunhua

AU - Wang, Kezhi

AU - Zhu, Huiling

AU - Wang, Jiangzhou

PY - 2019/12

Y1 - 2019/12

N2 - In this paper, the user cooperative task computation is explored by sharing the computing capability of the user equipments (UEs) so as to enhance the performance of mobile edge computing (MEC) networks. The number of completed tasks is maximized while minimizing the total power consumption of the UEs by jointly optimizing the user task offloading decision, the computational speed for the offloaded task and the transmit power for task offloading. An iterative algorithm based on the linear programming relaxation is proposed to solve the formulated mixed integer non-linear problem. The simulation results show that the proposed user cooperative computation scheme can achieve a higher completed tasks ratio than the noncooperative scheme.

AB - In this paper, the user cooperative task computation is explored by sharing the computing capability of the user equipments (UEs) so as to enhance the performance of mobile edge computing (MEC) networks. The number of completed tasks is maximized while minimizing the total power consumption of the UEs by jointly optimizing the user task offloading decision, the computational speed for the offloaded task and the transmit power for task offloading. An iterative algorithm based on the linear programming relaxation is proposed to solve the formulated mixed integer non-linear problem. The simulation results show that the proposed user cooperative computation scheme can achieve a higher completed tasks ratio than the noncooperative scheme.

KW - Task analysis

KW - servers

KW - power demand

KW - device-to-device communication

KW - Mobile handsets

KW - computational modeling

KW - edge computing

U2 - 10.1109/GLOBECOM38437.2019.9013629

DO - 10.1109/GLOBECOM38437.2019.9013629

M3 - Conference contribution

SN - 9781728109633

BT - Proceedings of the 2019 2019 IEEE Global Communications Conference (GLOBECOM), December 9 – 13, in Waikoloa, Hawaii, USA

PB - IEEE

CY - Piscataway, NJ

T2 - 2019 IEEE Global Communications Conference

Y2 - 9 December 2019 through 13 December 2019

ER -

Power Efficient User Cooperative Computation to Maximize Completed Tasks in MEC Networks

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