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 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.