Application of KNCMA Algorithm Integrating Network Features in Data Resource Scheduling

To improve the efficiency of data resource scheduling in virtual data centers, this study analyzes the network structure of the data center, constructs a set of key nodes, and selects the importance of nodes. And a mapping algorithm based on key nodesis proposed as the mapping algorithm for the virtual data center. The effectiveness of virtual data center compression and network feature validation are analyzed. In the DCell structure, after compressing the virtual data center, the request acceptance rate has improved. The compressed virtual data center significantly consumes less physical bandwidth during mapping compared to the case of direct virtual data center mapping. In the BCube structure, after compressing the virtual data center, the request acceptance rate has also improved. A comparative experiment is conducted between the mapping algorithm based on key nodes and the mapping algorithm based ontherandom experiments. Under the three architectures of DCell, BCube, and Fattree, the acceptance rate of key node mapping algorithms is higher than that ofthe random experimental mapping algorithms. Its bandwidth utilization rate is also lower than the mapping algorithm in random experiments. After introducing network features, the request acceptance rate has improved to varying degrees. The bandwidth utilization rate increases with the increase of acceptance rate.