Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm
Journal article
Thien, Phan Duc, Wu, Fan, Bekhit, Mahmoud, Fathalla, Ahmed and Salah, Ahmed. (2024). Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm. International Journal of Computational Intelligence Systems. 17(1), pp. 1-18. https://doi.org/10.1007/s44196-024-00430-x
Authors | Thien, Phan Duc, Wu, Fan, Bekhit, Mahmoud, Fathalla, Ahmed and Salah, Ahmed |
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Abstract | Virtual network functions (VNFs) have gradually replaced the implementation of traditional network functions. Through efficient placement, the VNF placement technology strives to operate VNFs consistently to the greatest extent possible within restricted resources. Thus, VNF mapping and scheduling tasks can be framed as an optimization problem. Existing research efforts focus only on optimizing the VNFs scheduling or mapping. Besides, the existing methods focus only on one or two objectives. In this work, we proposed addressing the problem of VNFs scheduling and mapping. This work proposed framing the problem of VNFs scheduling and mapping as a multi-objective optimization problem on three objectives, namely (1) minimizing line latency of network link, (2) reducing the processing capacity of each virtual machine, and (3) reducing the processing latency of virtual machines. Then, the proposed VNF-NSGA-III algorithm, an adapted variation of the NSGA-III algorithm, was used to solve this multi-objective problem. Our proposed algorithm has been thoroughly evaluated through a series of experiments on homogeneous and heterogeneous data center environments. The proposed method was compared to several heuristic and recent meta-heuristic methods. The results reveal that the VNF-NSGA-III outperformed the comparison methods. |
Keywords | Heuristic algorithms; Mapping and scheduling; Multi-objective optimization; NSGA-III; Virtual network functions; VNFs |
Year | 01 Jan 2024 |
Journal | International Journal of Computational Intelligence Systems |
Journal citation | 17 (1), pp. 1-18 |
Publisher | Springer Nature |
ISSN | 1875-6891 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s44196-024-00430-x |
Web address (URL) | https://link.springer.com/article/10.1007/s44196-024-00430-x |
Open access | Published as ‘gold’ (paid) open access |
Research or scholarly | Research |
Page range | 1-18 |
Author's accepted manuscript | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 11 Mar 2024 |
Publication process dates | |
Accepted | 03 Feb 2024 |
Deposited | 16 Jun 2024 |
Additional information | © The Author(s), 2024. |
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | |
Place of publication | France |
https://acuresearchbank.acu.edu.au/item/909z0/optimizing-placement-and-scheduling-for-vnf-by-a-multi-objective-optimization-genetic-algorithm
Download files
Author's accepted manuscript
OA_Bekhit_2024_Optimizing_Placement_and_Scheduling_for_VNF.pdf | |
License: CC BY 4.0 | |
File access level: Open |
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