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    Rights statement: This is the author’s version of a work that was accepted for publication in Future Generation Computer Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Future Generation Computer Systems, 85, 2018 DOI: 10.1016/j.future.2018.02.014

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User mobility aware task assignment for mobile edge computing

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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  • Zi Wang
  • Zhiwei Zhao
  • Geyong Min
  • Xinyuan Huang
  • Qiang Ni
  • Rong Wang
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<mark>Journal publication date</mark>08/2018
<mark>Journal</mark>Future Generation Computer Systems
Volume85
Number of pages8
Pages (from-to)1-8
Publication StatusPublished
Early online date16/03/18
<mark>Original language</mark>English

Abstract

Mobile Edge Computing (MEC) has emerged as a prospective computing paradigm to provide pervasive computing and storage services for mobile and big data applications. In MEC, many small cell base stations (sBSs) are deployed to establish a mobile edge network (MEN). These sBSs can be usually accessed directly by mobile users. The computational tasks are first offloaded from mobile users to the MEN and then executed in one or several specific sBSs in the MEN. While the offloading decision has been well studied, the task execution delay on the MEN side is overlooked. This paper aims at reducing the task execution delay by task scheduling in MENs. Specifically, we jointly consider the task properties, the user mobility and network constraints. The problem is formalized as a constraint satisfaction problem and a lightweight heuristic solution is proposed for fast scheduling. We conduct simulation experiments to study the performance of the proposed work. The results show that our work is able to significantly reduce the task execution delay in MENs and thus reduces the end-to-end delay for MEC tasks.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Future Generation Computer Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Future Generation Computer Systems, 85, 2018 DOI: 10.1016/j.future.2018.02.014