Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing

Ruixuan Wu; Yuliang Shi; Haitao Yuan; Jing Bi; Jia Zhang

doi:10.1109/SMC53992.2023.10394210

Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing

Ruixuan Wu
, Yuliang Shi
, Haitao Yuan
, Jing Bi
, Jia Zhang

School of Automation Science and Electrical Engineering

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

Abstract

Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time.

Original language	English
Title of host publication	2023 IEEE International Conference on Systems, Man, and Cybernetics
Subtitle of host publication	Improving the Quality of Life, SMC 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2289-2294
Number of pages	6
ISBN (Electronic)	9798350337020
DOIs	https://doi.org/10.1109/SMC53992.2023.10394210
State	Published - 2023
Event	2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023 - Hybrid, Honolulu, United States Duration: 1 Oct 2023 → 4 Oct 2023

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)	1062-922X

Conference

Conference	2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023
Country/Territory	United States
City	Hybrid, Honolulu
Period	1/10/23 → 4/10/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

computation offloading
energy optimization
Mobile edge computing
multi-population optimization
simulated annealing

Access to Document

10.1109/SMC53992.2023.10394210

Cite this

Wu, R., Shi, Y., Yuan, H., Bi, J., & Zhang, J. (2023). Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing. In 2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings (pp. 2289-2294). (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC53992.2023.10394210

Wu, Ruixuan ; Shi, Yuliang ; Yuan, Haitao et al. / Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing. 2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 2289-2294 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

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title = "Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing",

abstract = "Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time.",

keywords = "computation offloading, energy optimization, Mobile edge computing, multi-population optimization, simulated annealing",

author = "Ruixuan Wu and Yuliang Shi and Haitao Yuan and Jing Bi and Jia Zhang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023 ; Conference date: 01-10-2023 Through 04-10-2023",

year = "2023",

doi = "10.1109/SMC53992.2023.10394210",

language = "英语",

series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",

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

pages = "2289--2294",

booktitle = "2023 IEEE International Conference on Systems, Man, and Cybernetics",

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}

Wu, R, Shi, Y, Yuan, H, Bi, J & Zhang, J 2023, Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing. in 2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, Institute of Electrical and Electronics Engineers Inc., pp. 2289-2294, 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023, Hybrid, Honolulu, United States, 1/10/23. https://doi.org/10.1109/SMC53992.2023.10394210

Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing. / Wu, Ruixuan; Shi, Yuliang; Yuan, Haitao et al.
2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 2289-2294 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

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

TY - GEN

T1 - Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing

AU - Wu, Ruixuan

AU - Shi, Yuliang

AU - Yuan, Haitao

AU - Bi, Jing

AU - Zhang, Jia

PY - 2023

Y1 - 2023

N2 - Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time.

AB - Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time.

KW - computation offloading

KW - energy optimization

KW - Mobile edge computing

KW - multi-population optimization

KW - simulated annealing

UR - https://www.scopus.com/pages/publications/85187249617

U2 - 10.1109/SMC53992.2023.10394210

DO - 10.1109/SMC53992.2023.10394210

M3 - 会议稿件

AN - SCOPUS:85187249617

T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

SP - 2289

EP - 2294

BT - 2023 IEEE International Conference on Systems, Man, and Cybernetics

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

Y2 - 1 October 2023 through 4 October 2023

ER -

Wu R, Shi Y, Yuan H, Bi J, Zhang J. Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing. In 2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 2289-2294. (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). doi: 10.1109/SMC53992.2023.10394210

Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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