Improved grey wolf optimization algorithm based service function chain mapping algorithm
With the rise of new Internet applications such as the industrial Internet, the Internet of vehicles, and the metaverse, the network’s requirements for low latency, reliability, security, and certainty are facing severe challenges.In the process of virtual network deployment, when using network func...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2022-11-01
|
| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022275/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841530733423755264 |
|---|---|
| author | Yue ZHANG Junnan ZHANG Xiaochun WU Chen HONG Jingjing ZHOU |
| author_facet | Yue ZHANG Junnan ZHANG Xiaochun WU Chen HONG Jingjing ZHOU |
| author_sort | Yue ZHANG |
| collection | DOAJ |
| description | With the rise of new Internet applications such as the industrial Internet, the Internet of vehicles, and the metaverse, the network’s requirements for low latency, reliability, security, and certainty are facing severe challenges.In the process of virtual network deployment, when using network function virtualization technology, there were problems such as low service function chain mapping efficiency and high deployment resource overhead.The node activation cost and instantiation cost was jointly considered, an integer linear programming model with the optimization goal of minimizing the average deployment network cost was established, and an improved grey wolf optimization service function chain mapping (IMGWO-SFCM) algorithm was proposed.Three strategies: mapping scheme search based on acyclic KSP algorithm, mapping scheme coding and improvement based on reverse learning and nonlinear convergence were added to the standard grey wolf optimization algorithm to form this algorithm.The global search and local search capabilities were well balanced and the service function chain mapping scheme was quickly determined by IMGWO-SFCM.Compared with the comparison algorithm, IMGWO-SFCM reduces the average deployment network cost by 11.86% while ensuring a higher service function chain request acceptance rate. |
| format | Article |
| id | doaj-art-3298ad16ccb344cd965f31175d32b216 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2022-11-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-3298ad16ccb344cd965f31175d32b2162025-01-15T02:59:52ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012022-11-0138577259575268Improved grey wolf optimization algorithm based service function chain mapping algorithmYue ZHANGJunnan ZHANGXiaochun WUChen HONGJingjing ZHOUWith the rise of new Internet applications such as the industrial Internet, the Internet of vehicles, and the metaverse, the network’s requirements for low latency, reliability, security, and certainty are facing severe challenges.In the process of virtual network deployment, when using network function virtualization technology, there were problems such as low service function chain mapping efficiency and high deployment resource overhead.The node activation cost and instantiation cost was jointly considered, an integer linear programming model with the optimization goal of minimizing the average deployment network cost was established, and an improved grey wolf optimization service function chain mapping (IMGWO-SFCM) algorithm was proposed.Three strategies: mapping scheme search based on acyclic KSP algorithm, mapping scheme coding and improvement based on reverse learning and nonlinear convergence were added to the standard grey wolf optimization algorithm to form this algorithm.The global search and local search capabilities were well balanced and the service function chain mapping scheme was quickly determined by IMGWO-SFCM.Compared with the comparison algorithm, IMGWO-SFCM reduces the average deployment network cost by 11.86% while ensuring a higher service function chain request acceptance rate.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022275/network function virtualizationservice function chainresource optimization |
| spellingShingle | Yue ZHANG Junnan ZHANG Xiaochun WU Chen HONG Jingjing ZHOU Improved grey wolf optimization algorithm based service function chain mapping algorithm Dianxin kexue network function virtualization service function chain resource optimization |
| title | Improved grey wolf optimization algorithm based service function chain mapping algorithm |
| title_full | Improved grey wolf optimization algorithm based service function chain mapping algorithm |
| title_fullStr | Improved grey wolf optimization algorithm based service function chain mapping algorithm |
| title_full_unstemmed | Improved grey wolf optimization algorithm based service function chain mapping algorithm |
| title_short | Improved grey wolf optimization algorithm based service function chain mapping algorithm |
| title_sort | improved grey wolf optimization algorithm based service function chain mapping algorithm |
| topic | network function virtualization service function chain resource optimization |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022275/ |
| work_keys_str_mv | AT yuezhang improvedgreywolfoptimizationalgorithmbasedservicefunctionchainmappingalgorithm AT junnanzhang improvedgreywolfoptimizationalgorithmbasedservicefunctionchainmappingalgorithm AT xiaochunwu improvedgreywolfoptimizationalgorithmbasedservicefunctionchainmappingalgorithm AT chenhong improvedgreywolfoptimizationalgorithmbasedservicefunctionchainmappingalgorithm AT jingjingzhou improvedgreywolfoptimizationalgorithmbasedservicefunctionchainmappingalgorithm |