Files and links (1)
Published VersionCC BY-NC-ND V4.0, Open Access
Journal article
Adaptive delay-energy balanced partial offloading strategy in Mobile Edge Computing networks
Digital Communications and Networks, Vol.9(6), pp.1310-1318
2023
Abstract
Mobile Edge Computing (MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy requirements. In this paper, we propose a flexible MEC-based requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy consumption. To address the dimensional differences between time and energy, we introduce two normalized parameters and then derive the computational overhead of processing tasks. Different from existing works, this paper considers practical variations in the user request patterns, and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay, task workload and power constraints. Since the resulting problem is non-convex, we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading solution. Numerical experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.
Details
- Title
- Adaptive delay-energy balanced partial offloading strategy in Mobile Edge Computing networks
- Authors
- Shumei Liu (Author) - Northeastern UniversityYao Yu (Corresponding Author) - Northeastern UniversityLei Guo - Chongqing University of Posts and TelecommunicationsPhee Lep Yeoh - The University of SydneyBranka Vucetic - The University of SydneyYonghui Li - The University of Sydney
- Publication details
- Digital Communications and Networks, Vol.9(6), pp.1310-1318
- Publisher
- KeAi Publishing Communications Ltd.
- Date published
- 2023
- DOI
- 10.1016/j.dcan.2022.05.029
- ISSN
- 2352-8648; 2468-5925
- Copyright note
- © 2022 Chongqing University of Posts and Telecommunications. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
- Grant note
- This work was supported in part by the National Natural Science Foundation of China under Grant 62171113 and 61941113, and in part by the Fundamental Research Funds for the Central Universities under Grant N2116003 and N2116011.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991198947602621
- Output Type
- Journal article
Metrics
1 Record Views