1. Multi-tenant computing network resource allocation algorithm based on deep reinforcement learning
- Author
-
HU Yuxiang, FENG Xu, DONG Yongji, HE Mengyang, ZHUANG Lei, and SONG Yanrui
- Subjects
integration of computing and networking ,computing power network ,resource scheduling ,multi objective optimization ,deep reinforcement learning ,Information technology ,T58.5-58.64 ,Management information systems ,T58.6-58.62 - Abstract
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.
- Published
- 2024
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