Multi-tenant computing network resource allocation algorithm based on deep reinforcement learning
With the rapid advancement of intelligent businesses, the pre-existing relationship between traditional network architectures and computing capabilities has made it difficult to meet the current demands, making the implementation of computing-network convergence inevitable. Under the new computing p...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2024-12-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00446/ |
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| _version_ | 1832586338726051840 |
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| author | HU Yuxiang FENG Xu DONG Yongji HE Mengyang ZHUANG Lei SONG Yanrui |
| author_facet | HU Yuxiang FENG Xu DONG Yongji HE Mengyang ZHUANG Lei SONG Yanrui |
| author_sort | HU Yuxiang |
| collection | DOAJ |
| description | With the rapid advancement of intelligent businesses, the pre-existing relationship between traditional network architectures and computing capabilities has made it difficult to meet the current demands, making the implementation of computing-network convergence inevitable. Under the new computing power network framework brought about by the convergence of computing networks, efficient and intelligent resource scheduling strategy has become a key link to improve user experience. However, the existing resource scheduling algorithms have a single optimization objective and cannot meet the differentiated business needs of multi-tenants. To this end, a Multi objective deep reinforcement learning resource scheduling (MODRLRS) was proposed to call the computing resources and network resources in the computing power network. The algorithm performs multi-objective scheduling optimization of computing network resources by constructing a Pareto optimal solution set to meet the personalized business needs of different tenants. Simulation experimental results show that compared with other multi-objective resource scheduling algorithms, the proposed algorithm improves the request acceptance rate by 4.9% and the compliant delay request rate by 4.78%, which can flexibly adapt to the unique requirements of various computing services. |
| format | Article |
| id | doaj-art-68e31d391c35417885742dc06836d275 |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-68e31d391c35417885742dc06836d2752025-01-25T19:00:31ZzhoChina InfoCom Media Group物联网学报2096-37502024-12-018344479606576Multi-tenant computing network resource allocation algorithm based on deep reinforcement learningHU YuxiangFENG XuDONG YongjiHE MengyangZHUANG LeiSONG YanruiWith the rapid advancement of intelligent businesses, the pre-existing relationship between traditional network architectures and computing capabilities has made it difficult to meet the current demands, making the implementation of computing-network convergence inevitable. Under the new computing power network framework brought about by the convergence of computing networks, efficient and intelligent resource scheduling strategy has become a key link to improve user experience. However, the existing resource scheduling algorithms have a single optimization objective and cannot meet the differentiated business needs of multi-tenants. To this end, a Multi objective deep reinforcement learning resource scheduling (MODRLRS) was proposed to call the computing resources and network resources in the computing power network. The algorithm performs multi-objective scheduling optimization of computing network resources by constructing a Pareto optimal solution set to meet the personalized business needs of different tenants. Simulation experimental results show that compared with other multi-objective resource scheduling algorithms, the proposed algorithm improves the request acceptance rate by 4.9% and the compliant delay request rate by 4.78%, which can flexibly adapt to the unique requirements of various computing services.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00446/integration of computing and networkingcomputing power networkresource schedulingmulti objective optimizationdeep reinforcement learning |
| spellingShingle | HU Yuxiang FENG Xu DONG Yongji HE Mengyang ZHUANG Lei SONG Yanrui Multi-tenant computing network resource allocation algorithm based on deep reinforcement learning 物联网学报 integration of computing and networking computing power network resource scheduling multi objective optimization deep reinforcement learning |
| title | Multi-tenant computing network resource allocation algorithm based on deep reinforcement learning |
| title_full | Multi-tenant computing network resource allocation algorithm based on deep reinforcement learning |
| title_fullStr | Multi-tenant computing network resource allocation algorithm based on deep reinforcement learning |
| title_full_unstemmed | Multi-tenant computing network resource allocation algorithm based on deep reinforcement learning |
| title_short | Multi-tenant computing network resource allocation algorithm based on deep reinforcement learning |
| title_sort | multi tenant computing network resource allocation algorithm based on deep reinforcement learning |
| topic | integration of computing and networking computing power network resource scheduling multi objective optimization deep reinforcement learning |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00446/ |
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