Multiobjective Optimized Cloudlet Deployment and Task Offloading for Mobile-Edge Computing

Xiaojian Zhu, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Mobile-edge computing provides an effective approach to reducing the workload of smart devices and the network delay induced by data transfer through deploying computational resources in the proximity of the devices. In a mobile-edge computing system, it is of great importance to improve the quality of experience of users and reduce the deployment cost for service providers. This article investigates a joint cloudlet deployment and task offloading problem with the objectives of minimizing energy consumption and task response delay of users and the number of deployed cloudlets. Since it is a multiobjective optimization problem, a set of tradeoff solutions ought to be found. After formulating this problem as a mixed-integer nonlinear program and proving its NP-completeness, we propose a modified guided population archive whale optimization algorithm to solve it. The superiority of our devised algorithm over other methods is confirmed through extensive simulations.

Original languageEnglish (US)
Pages (from-to)15582-15595
Number of pages14
JournalIEEE Internet of Things Journal
Volume8
Issue number20
DOIs
StatePublished - Oct 15 2021

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Keywords

  • Communication
  • Internet of Things (IoT)
  • mobile-edge computing
  • multiobjective optimization
  • offloading

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