Bozorgchenani, A. orcid.org/0000-0003-1360-6952, Mashhadi, F. orcid.org/0000-0001-6570-2095, Tarchi, D. orcid.org/0000-0001-7338-1957 et al. (1 more author) (2021) Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments. IEEE Transactions on Mobile Computing, 20 (10). pp. 2992-3005. ISSN 1536-1233
Abstract
In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Mobile edge computing, fog computing, computation sharing, NSGA2, multi-objective optimization, evolutionary algorithms, energy consumption, delay |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 Aug 2023 12:55 |
Last Modified: | 01 Sep 2023 13:41 |
Published Version: | https://ieeexplore.ieee.org/document/9091902 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/tmc.2020.2994232 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:201529 |