@inbook{5a75c016e76547948df481b7ca1dc92b,
title = "Multi-objective Optimization Approach to High-Performance Cloudlet Deployment and Task Offloading in Mobile Edge Computing",
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 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.",
keywords = "Cloudlet deployment, Communication, Cost, Delay, Energy consumption, Internet of Things, Mobile-edge computing, Multiobjective optimization, Offloading, Pareto optimal solutions",
author = "Xiaojian Zhu and Zhou, {Meng Chu}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.",
year = "2024",
doi = "10.1007/978-3-031-42194-5_6",
language = "English (US)",
series = "Internet of Things",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "89--119",
booktitle = "Internet of Things",
address = "Germany",
}