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Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it...

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Autores principales: Huang, Mengxing, Zhai, Qianhao, Chen, Yinjie, Feng, Siling, Shu, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070405/
https://www.ncbi.nlm.nih.gov/pubmed/33918037
http://dx.doi.org/10.3390/s21082628
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author Huang, Mengxing
Zhai, Qianhao
Chen, Yinjie
Feng, Siling
Shu, Feng
author_facet Huang, Mengxing
Zhai, Qianhao
Chen, Yinjie
Feng, Siling
Shu, Feng
author_sort Huang, Mengxing
collection PubMed
description Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.
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spelling pubmed-80704052021-04-26 Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing Huang, Mengxing Zhai, Qianhao Chen, Yinjie Feng, Siling Shu, Feng Sensors (Basel) Article Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions. MDPI 2021-04-08 /pmc/articles/PMC8070405/ /pubmed/33918037 http://dx.doi.org/10.3390/s21082628 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Mengxing
Zhai, Qianhao
Chen, Yinjie
Feng, Siling
Shu, Feng
Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing
title Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing
title_full Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing
title_fullStr Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing
title_full_unstemmed Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing
title_short Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing
title_sort multi-objective whale optimization algorithm for computation offloading optimization in mobile edge computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070405/
https://www.ncbi.nlm.nih.gov/pubmed/33918037
http://dx.doi.org/10.3390/s21082628
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