Multi-objective Optimization Approach to High-Performance Cloudlet Deployment and Task Offloading in Mobile Edge Computing

Xiaojian Zhu, Meng Chu Zhou

Research output: Chapter in Book/Report/Conference proceedingChapter


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 chapter 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 multi-objective optimization problem, a set of trade-off 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)
Title of host publicationInternet of Things
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages31
StatePublished - 2024

Publication series

NameInternet of Things
VolumePart F1719
ISSN (Print)2199-1073
ISSN (Electronic)2199-1081

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Instrumentation
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics
  • Artificial Intelligence


  • Cloudlet deployment
  • Communication
  • Cost
  • Delay
  • Energy consumption
  • Internet of Things
  • Mobile-edge computing
  • Multiobjective optimization
  • Offloading
  • Pareto optimal solutions


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