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International Conference on Algorithms and Architectures for Parallel Processing

ICA3PP 2018: Algorithms and Architectures for Parallel Processing pp 460–475Cite as

Energy-Efficient Offloading in Mobile Edge Computing with Edge-Cloud Collaboration

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11336))

Abstract

Multiple access mobile edge computing is an emerging technique to bring computation resources close to end mobile users. By deploying edge servers at WiFi access points or cellular base stations, the computation capabilities of mobile users can be extended. Existing works mostly assume the remote cloud server can be viewed as a special edge server or the edge servers are willing to cooperate, which is not practical. In this work, we propose an edge-cloud cooperative architecture where edge servers can rent for the remote cloud servers to expedite the computation of tasks from mobile users. With this architecture, the computation offloading problem is modeled as a mixed integer programming with delay constraints, which is NP-hard. The objective is to minimize the total energy consumption of mobile devices. We propose a greedy algorithm with approximation radio of \((1+\varepsilon )\) as well as a simulated annealing algorithm to effectively solve the problem. Extensive simulation results demonstrate that, the proposed greedy algorithm can achieve the same application completing time budget performance of the Brute Force optional algorithm with only 31% extra energy cost.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61702115 and 61672171, Natural Science Foundation of Guangdong, China under Grant No. 2018B030311007, and Major R&D Project of Educational Commission of Guangdong under Grant No. 2016KZDXM052. This work was also supported by China Postdoctoral Science Foundation Fund under Grant No. 2017M622632. The corresponding author is Jigang Wu (asjgwucn@outlook.com).

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Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

    Xin Long, Jigang Wu & Long Chen

Authors
  1. Xin Long
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  2. Jigang Wu
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  3. Long Chen
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Corresponding author

Correspondence to Jigang Wu .

Editor information

Editors and Affiliations

  1. Rutgers University–Newark, Newark, NJ, USA

    Jaideep Vaidya

  2. Guangzhou University, Guangzhou, China

    Jin Li

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Long, X., Wu, J., Chen, L. (2018). Energy-Efficient Offloading in Mobile Edge Computing with Edge-Cloud Collaboration. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11336. Springer, Cham. https://doi.org/10.1007/978-3-030-05057-3_35

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  • DOI: https://doi.org/10.1007/978-3-030-05057-3_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05056-6

  • Online ISBN: 978-3-030-05057-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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