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A Lightweight Energy-Efficient Computational Offloading Scheme in Mobile Edge Computing

Authors:

Weigang Zhang,

Biyu Zhou,

Weixia Dang,

Songlin HuAuthors Info & Claims

e-Energy '20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems

Pages 560 - 565

https://doi.org/10.1145/3396851.3402922

Published: 18 June 2020 Publication History

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Abstract

Mobile edge computing (MEC) has been an alternative to mobile cloud computing (MCC) for computationally intensive mobile tasks by offloading computations to nearby servers. However, it is not easy to generate an optimal offloading scheme considering both energy consumption and time delay with low time complexity. In this paper, we propose a lightweight energy-efficient computational offloading scheme (LEEOS) for a task to make the offloading decision of each component. First, LEEOS calculates the cost values of local execution and remote execution for all components. Based on these cost values, it uses a greedy heuristic to determine which components to offload to mobile edge servers for execution. Experiment results show that our proposed approach is promising in terms of energy consumption of user equipment as well as computation time.

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Cited By

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Xue MWu HLi RXu MJiao P(2022)EosDNN: An Efficient Offloading Scheme for DNN Inference Acceleration in Local-Edge-Cloud Collaborative EnvironmentsIEEE Transactions on Green Communications and Networking10.1109/TGCN.2021.31117316:1(248-264)Online publication date: Mar-2022
https://doi.org/10.1109/TGCN.2021.3111731

Index Terms

A Lightweight Energy-Efficient Computational Offloading Scheme in Mobile Edge Computing
1. Networks
  1. Network services
    1. Cloud computing
  2. Network types
    1. Mobile networks
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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e-Energy '20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems

June 2020

601 pages

ISBN:9781450380096

DOI:10.1145/3396851

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 18 June 2020

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SIGEnergy

e-Energy '20: The Eleventh ACM International Conference on Future Energy Systems

June 22 - 26, 2020

Virtual Event, Australia

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e-Energy '20 Paper Acceptance Rate 77 of 173 submissions, 45%;

Overall Acceptance Rate 160 of 446 submissions, 36%

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Xue MWu HLi RXu MJiao P(2022)EosDNN: An Efficient Offloading Scheme for DNN Inference Acceleration in Local-Edge-Cloud Collaborative EnvironmentsIEEE Transactions on Green Communications and Networking10.1109/TGCN.2021.31117316:1(248-264)Online publication date: Mar-2022
https://doi.org/10.1109/TGCN.2021.3111731

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Modelling Task Offloading Mobile Edge Computing

Energy Efficient Computational Offloading Framework for Mobile Cloud Computing

A lightweight active service migration framework for computational offloading in mobile cloud computing

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