Cloud computing-oriented big data analysis-based intelligent university talent development mechanism

Abstract

The arrival of big data has changed and affected people's lives. To achieve better development, universities must attach importance to applying big data for talent development. An improved k-nearest neighbors density peak clustering algorithm (iKDPC) combined with principal components is suggested to enhance the impact of hierarchical classification in talent development in universities. For the hybrid wired/wireless data center network, we propose a virtual node fusion-based virtual network embedding with the wireless links assistance method (VnfVNE-WLA). The experimental results show that iKDPC can effectively classify talents in universities and provide scientific and quantitative references for scientifically and rationally exploring the classification issues in developing talents in universities. Moreover, the simulation results demonstrate that the VnfVNE-WLA method can successfully increase the utilization rate of talent resources from the virtual network to the physical network to better support talent development in universities using cloud computing.