An optimized greedy-based task offloading method for mobile edge computing

Wei Zhou; Chuangwei Lin; Jirun Duan; Ke Ren; Xuyun Zhang; Wanchun Dou

doi:10.1007/978-3-030-95384-3_31

An optimized greedy-based task offloading method for mobile edge computing

Wei Zhou, Chuangwei Lin, Jirun Duan, Ke Ren, Xuyun Zhang, Wanchun Dou^*

^*Corresponding author for this work

School of Computing

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

7 Citations (Scopus)

Abstract

With the development of smart mobile devices (SMDs), computationally intensive and latency-sensitive applications are emerging. However, Mobile devices have limited processing power by nature. To overcome this problem, mobile edge computing enables users to offload tasks to proximal edge servers for faster task computation. Most studies in task offloading consider stable systems and ignore the number of tasks fluctuating over time. Poor offloading decisions will overload edge servers during peak periods, which leads to significantly high latency. To address this challenge, an optimized greedy-based offloading method (OGOM) is designed to offload tasks. OGOM adopts different offloading strategies depending on the server load factor. When edge servers are highly loaded, OGOM offloads some of the tasks to more idle servers instead of the servers with the lowest theoretical latency to achieve load balancing. Simulation results show that the OGOM is effective in avoiding edge server overload. In addition, OGOM reduces latency by an average of 20% compared to the normal greedy-based offloading method.

Original language	English
Title of host publication	Algorithms and Architectures for Parallel Processing
Subtitle of host publication	21st International Conference, ICA3PP 2021, Virtual Event, December 3–5, 2021, Proceedings, Part I
Editors	Yongxuan Lai, Tian Wang, Min Jiang, Guangquan Xu, Wei Liang, Aniello Castiglione
Place of Publication	Cham, Switzerland
Publisher	Springer, Springer Nature
Pages	494-508
Number of pages	15
ISBN (Electronic)	9783030953843
ISBN (Print)	9783030953836
DOIs	https://doi.org/10.1007/978-3-030-95384-3_31
Publication status	Published - 2022
Event	21st International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2021 - Virtual, Online Duration: 3 Dec 2021 → 5 Dec 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13155
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2021
City	Virtual, Online
Period	3/12/21 → 5/12/21

Keywords

Mobile edge computing
Greedy-based method
Task offloading
Load factor

Access to Document

10.1007/978-3-030-95384-3_31

Cite this

Zhou, W., Lin, C., Duan, J., Ren, K., Zhang, X., & Dou, W. (2022). An optimized greedy-based task offloading method for mobile edge computing. In Y. Lai, T. Wang, M. Jiang, G. Xu, W. Liang, & A. Castiglione (Eds.), Algorithms and Architectures for Parallel Processing: 21st International Conference, ICA3PP 2021, Virtual Event, December 3–5, 2021, Proceedings, Part I (pp. 494-508). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13155). Springer, Springer Nature. https://doi.org/10.1007/978-3-030-95384-3_31

Zhou, Wei ; Lin, Chuangwei ; Duan, Jirun et al. / An optimized greedy-based task offloading method for mobile edge computing. Algorithms and Architectures for Parallel Processing: 21st International Conference, ICA3PP 2021, Virtual Event, December 3–5, 2021, Proceedings, Part I. editor / Yongxuan Lai ; Tian Wang ; Min Jiang ; Guangquan Xu ; Wei Liang ; Aniello Castiglione. Cham, Switzerland : Springer, Springer Nature, 2022. pp. 494-508 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "With the development of smart mobile devices (SMDs), computationally intensive and latency-sensitive applications are emerging. However, Mobile devices have limited processing power by nature. To overcome this problem, mobile edge computing enables users to offload tasks to proximal edge servers for faster task computation. Most studies in task offloading consider stable systems and ignore the number of tasks fluctuating over time. Poor offloading decisions will overload edge servers during peak periods, which leads to significantly high latency. To address this challenge, an optimized greedy-based offloading method (OGOM) is designed to offload tasks. OGOM adopts different offloading strategies depending on the server load factor. When edge servers are highly loaded, OGOM offloads some of the tasks to more idle servers instead of the servers with the lowest theoretical latency to achieve load balancing. Simulation results show that the OGOM is effective in avoiding edge server overload. In addition, OGOM reduces latency by an average of 20\% compared to the normal greedy-based offloading method.",

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Zhou, W, Lin, C, Duan, J, Ren, K, Zhang, X & Dou, W 2022, An optimized greedy-based task offloading method for mobile edge computing. in Y Lai, T Wang, M Jiang, G Xu, W Liang & A Castiglione (eds), Algorithms and Architectures for Parallel Processing: 21st International Conference, ICA3PP 2021, Virtual Event, December 3–5, 2021, Proceedings, Part I. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13155, Springer, Springer Nature, Cham, Switzerland, pp. 494-508, 21st International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2021, Virtual, Online, 3/12/21. https://doi.org/10.1007/978-3-030-95384-3_31

An optimized greedy-based task offloading method for mobile edge computing. / Zhou, Wei; Lin, Chuangwei; Duan, Jirun et al.
Algorithms and Architectures for Parallel Processing: 21st International Conference, ICA3PP 2021, Virtual Event, December 3–5, 2021, Proceedings, Part I. ed. / Yongxuan Lai; Tian Wang; Min Jiang; Guangquan Xu; Wei Liang; Aniello Castiglione. Cham, Switzerland: Springer, Springer Nature, 2022. p. 494-508 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13155).

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

TY - GEN

T1 - An optimized greedy-based task offloading method for mobile edge computing

AU - Zhou, Wei

AU - Lin, Chuangwei

AU - Duan, Jirun

AU - Ren, Ke

AU - Zhang, Xuyun

AU - Dou, Wanchun

PY - 2022

Y1 - 2022

N2 - With the development of smart mobile devices (SMDs), computationally intensive and latency-sensitive applications are emerging. However, Mobile devices have limited processing power by nature. To overcome this problem, mobile edge computing enables users to offload tasks to proximal edge servers for faster task computation. Most studies in task offloading consider stable systems and ignore the number of tasks fluctuating over time. Poor offloading decisions will overload edge servers during peak periods, which leads to significantly high latency. To address this challenge, an optimized greedy-based offloading method (OGOM) is designed to offload tasks. OGOM adopts different offloading strategies depending on the server load factor. When edge servers are highly loaded, OGOM offloads some of the tasks to more idle servers instead of the servers with the lowest theoretical latency to achieve load balancing. Simulation results show that the OGOM is effective in avoiding edge server overload. In addition, OGOM reduces latency by an average of 20% compared to the normal greedy-based offloading method.

AB - With the development of smart mobile devices (SMDs), computationally intensive and latency-sensitive applications are emerging. However, Mobile devices have limited processing power by nature. To overcome this problem, mobile edge computing enables users to offload tasks to proximal edge servers for faster task computation. Most studies in task offloading consider stable systems and ignore the number of tasks fluctuating over time. Poor offloading decisions will overload edge servers during peak periods, which leads to significantly high latency. To address this challenge, an optimized greedy-based offloading method (OGOM) is designed to offload tasks. OGOM adopts different offloading strategies depending on the server load factor. When edge servers are highly loaded, OGOM offloads some of the tasks to more idle servers instead of the servers with the lowest theoretical latency to achieve load balancing. Simulation results show that the OGOM is effective in avoiding edge server overload. In addition, OGOM reduces latency by an average of 20% compared to the normal greedy-based offloading method.

KW - Mobile edge computing

KW - Greedy-based method

KW - Task offloading

KW - Load factor

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DO - 10.1007/978-3-030-95384-3_31

M3 - Conference proceeding contribution

AN - SCOPUS:85126224381

SN - 9783030953836

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Algorithms and Architectures for Parallel Processing

A2 - Lai, Yongxuan

A2 - Wang, Tian

A2 - Jiang, Min

A2 - Xu, Guangquan

A2 - Liang, Wei

A2 - Castiglione, Aniello

PB - Springer, Springer Nature

CY - Cham, Switzerland

T2 - 21st International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2021

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Zhou W, Lin C, Duan J, Ren K, Zhang X, Dou W. An optimized greedy-based task offloading method for mobile edge computing. In Lai Y, Wang T, Jiang M, Xu G, Liang W, Castiglione A, editors, Algorithms and Architectures for Parallel Processing: 21st International Conference, ICA3PP 2021, Virtual Event, December 3–5, 2021, Proceedings, Part I. Cham, Switzerland: Springer, Springer Nature. 2022. p. 494-508. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-95384-3_31

An optimized greedy-based task offloading method for mobile edge computing

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