Simulation of Performance Evaluation Model of Cloud Computing Platform Based on Deep Learning

With the wide application of cloud computing platform, more and more enterprises and individuals migrate their data to cloud computing platform. However, how to accurately evaluate and measure its performance and ensure the quality of service of users while making full use of resources is still an important problem to be solved urgently in the development of cloud computing platform. In this paper, a performance evaluation model of cloud computing platform based on deep learning is proposed. Based on BPNN (BP neural network) model, the complex nonlinear relationship between system input characteristics and service efficiency is simulated, and the efficiency model based on BPNN is established. The structure of the model mainly includes input module, data hiding module and output module. In this paper, a three-layer BPNN model is adopted, and based on the overall architecture of the intelligent service framework, the key input features affecting the overall service efficiency are extracted from each layer of the framework, and the overall service efficiency is predicted by using the BPNN model. The simulation results show that the prediction value of service efficiency based on BPNN optimization model is closer to the measured value than the traditional model without optimization algorithm. The experimental results show that the optimization model based on BPNN proposed in this paper has good computational efficiency and accuracy as a prediction model. It is proved that it can accurately evaluate the performance of cloud computing platform in practical application.