Energy Minimization and Offloading Number Maximization in Wireless Mobile Edge Computing

Peifeng Li; Yuansheng Luo; Kezhi Wang; Kun Yang

doi:10.1109/GLOCOM.2018.8647904

Energy Minimization and Offloading Number Maximization in Wireless Mobile Edge Computing

Peifeng Li, Yuansheng Luo, Kezhi Wang, Kun Yang

Computer and Information Sciences Department

Research output: Contribution to conference › Paper › peer-review

6 Citations (Scopus)

24 Downloads (Pure)

Abstract

With the fast development of mobile edge computing (MEC), user equipments (UEs) can enjoy much higher experience than before by offloading the tasks to its close edge cloud. In this paper, we assume there are several edge clouds, each of which has limited resource. We aim to maximize the number of offloaded tasks and minimize the energy consumption of all the UEs and edge clouds, by selecting the best edge cloud for each UE to offload. We formulate the problem as a mixed-integer non-convex optimization, which is difficult to solve in general. By transforming this problem into a minimum-cost maximum-flow (MCMF) problem, we can solve it efficiently. The simulation shows that our proposed algorithm has better performance and lower complexity than the conventional solutions.

Original language	English
DOIs	https://doi.org/10.1109/GLOCOM.2018.8647904
Publication status	Published - Dec 2018
Event	IEEE Global Communications Conference - Abu Dhabi National Exhibition Centre, Abu Dhabi, United Arab Emirates Duration: 9 Dec 2018 → 13 Dec 2018 http://globecom2018.ieee-globecom.org/

Conference

Conference	IEEE Global Communications Conference
Abbreviated title	GLOBECOM 2018
Country/Territory	United Arab Emirates
City	Abu Dhabi
Period	9/12/18 → 13/12/18
Internet address	http://globecom2018.ieee-globecom.org/

Keywords

Energy Minimization
Offloading Number Maximization
Mobile Edge Computing
Minimum-Cost-Maximum-Flow

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10.1109/GLOCOM.2018.8647904

Li et al - Energy Minimization and Offloading Number Maximization in Wireless Mobile Edge Computing AAMAccepted author manuscript, 208 KB

Cite this

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title = "Energy Minimization and Offloading Number Maximization in Wireless Mobile Edge Computing",

abstract = "With the fast development of mobile edge computing (MEC), user equipments (UEs) can enjoy much higher experience than before by offloading the tasks to its close edge cloud. In this paper, we assume there are several edge clouds, each of which has limited resource. We aim to maximize the number of offloaded tasks and minimize the energy consumption of all the UEs and edge clouds, by selecting the best edge cloud for each UE to offload. We formulate the problem as a mixed-integer non-convex optimization, which is difficult to solve in general. By transforming this problem into a minimum-cost maximum-flow (MCMF) problem, we can solve it efficiently. The simulation shows that our proposed algorithm has better performance and lower complexity than the conventional solutions.",

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author = "Peifeng Li and Yuansheng Luo and Kezhi Wang and Kun Yang",

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AU - Li, Peifeng

AU - Luo, Yuansheng

AU - Wang, Kezhi

AU - Yang, Kun

PY - 2018/12

Y1 - 2018/12

N2 - With the fast development of mobile edge computing (MEC), user equipments (UEs) can enjoy much higher experience than before by offloading the tasks to its close edge cloud. In this paper, we assume there are several edge clouds, each of which has limited resource. We aim to maximize the number of offloaded tasks and minimize the energy consumption of all the UEs and edge clouds, by selecting the best edge cloud for each UE to offload. We formulate the problem as a mixed-integer non-convex optimization, which is difficult to solve in general. By transforming this problem into a minimum-cost maximum-flow (MCMF) problem, we can solve it efficiently. The simulation shows that our proposed algorithm has better performance and lower complexity than the conventional solutions.

AB - With the fast development of mobile edge computing (MEC), user equipments (UEs) can enjoy much higher experience than before by offloading the tasks to its close edge cloud. In this paper, we assume there are several edge clouds, each of which has limited resource. We aim to maximize the number of offloaded tasks and minimize the energy consumption of all the UEs and edge clouds, by selecting the best edge cloud for each UE to offload. We formulate the problem as a mixed-integer non-convex optimization, which is difficult to solve in general. By transforming this problem into a minimum-cost maximum-flow (MCMF) problem, we can solve it efficiently. The simulation shows that our proposed algorithm has better performance and lower complexity than the conventional solutions.

KW - Energy Minimization

KW - Offloading Number Maximization

KW - Mobile Edge Computing

KW - Minimum-Cost-Maximum-Flow

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DO - 10.1109/GLOCOM.2018.8647904

M3 - Paper

T2 - IEEE Global Communications Conference

Y2 - 9 December 2018 through 13 December 2018

ER -

Energy Minimization and Offloading Number Maximization in Wireless Mobile Edge Computing

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Keywords

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