Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm
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 sch...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-03-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-024-00430-x |
| _version_ | 1797259040542162944 |
|---|---|
| author | Phan Duc Thien Fan Wu Mahmoud Bekhit Ahmed Fathalla Ahmad Salah |
| author_facet | Phan Duc Thien Fan Wu Mahmoud Bekhit Ahmed Fathalla Ahmad Salah |
| author_sort | Phan Duc Thien |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T23:03:06Z |
| format | Article |
| id | doaj.art-e21275d95ed3454597d7431ded00015a |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-24T23:03:06Z |
| publishDate | 2024-03-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-e21275d95ed3454597d7431ded00015a2024-03-17T12:37:49ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832024-03-0117111810.1007/s44196-024-00430-xOptimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic AlgorithmPhan Duc Thien0Fan Wu1Mahmoud Bekhit2Ahmed Fathalla3Ahmad Salah4College of Information Science and Engineering, Hunan UniversityCollege of Information Science and Engineering, Hunan UniversityUniversity of Technology of SydneyDepartment of Mathematics, Faculty of Science, Suez Canal UniversityCollege of Computing and Information Sciences, University of Technology and Applied SciencesAbstract 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.https://doi.org/10.1007/s44196-024-00430-xHeuristic algorithmsMapping and schedulingMulti-objective optimizationNSGA-IIIVirtual network functionsVNFs |
| spellingShingle | Phan Duc Thien Fan Wu Mahmoud Bekhit Ahmed Fathalla Ahmad Salah Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm International Journal of Computational Intelligence Systems Heuristic algorithms Mapping and scheduling Multi-objective optimization NSGA-III Virtual network functions VNFs |
| title | Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm |
| title_full | Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm |
| title_fullStr | Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm |
| title_full_unstemmed | Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm |
| title_short | Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm |
| title_sort | optimizing placement and scheduling for vnf by a multi objective optimization genetic algorithm |
| topic | Heuristic algorithms Mapping and scheduling Multi-objective optimization NSGA-III Virtual network functions VNFs |
| url | https://doi.org/10.1007/s44196-024-00430-x |
| work_keys_str_mv | AT phanducthien optimizingplacementandschedulingforvnfbyamultiobjectiveoptimizationgeneticalgorithm AT fanwu optimizingplacementandschedulingforvnfbyamultiobjectiveoptimizationgeneticalgorithm AT mahmoudbekhit optimizingplacementandschedulingforvnfbyamultiobjectiveoptimizationgeneticalgorithm AT ahmedfathalla optimizingplacementandschedulingforvnfbyamultiobjectiveoptimizationgeneticalgorithm AT ahmadsalah optimizingplacementandschedulingforvnfbyamultiobjectiveoptimizationgeneticalgorithm |