Heterogeneous GPU clusters play an important role in processing parallel applications and massive data sets in the cloud platform. However, due to the diversity of GPU types, how to effectively allocate various GPU types is a challenge. This paper first analyzes the characteristics of request and allocation for various GPU types based on Alibaba cluster data. Then we propose a method to adaptively select the best model to predict demand of various GPU types, and feature extraction from the best model. Further, we design a model based on Long Short Term Memory (LSTM) to forecast allocation of each GPU type. Finally, the extensive experimental results demonstrate the promising performance compared with multiple baseline methods by using real-world trace data from Alibaba cloud data centers. The trial illustrates that the demand prediction accuracy of the adaptive selection method reaches 87%, while the proposed prediction allocation model yields better performances with root mean square error and mean absolute error of 1.78 and 0.85, respectively.

Nowadays, poverty-stricken college students have become a special group among college students and occupied a higher proportion in it. How to accurately identify poverty levels of college students and provide funding is a new problem for universities. In this study, a novel model, which incorporated Random Forest with Principle Components Analysis (RF-PCA), is proposed to predict poverty levels of college students. To establish this model, we collect some useful information is to construct the datasets which include 4 classes of poverty levels and 21 features of poverty-stricken college students. Furthermore, the feature dimension reduction consists of two steps: the first step is to select the top 16 features with the ranking of feature, according to the Gini importance and Shapley Additive explanations (SHAP) values of features based on Random Forest (RF) model; the second step is to extract 11 dimensions by means of Principle Components Analysis (PCA). Subsequently, confusion metrics and receiver operating characteristic (ROC) curves are utilized to evaluate the promising performance of the proposed model. Especially the accuracy of the model achieves 78.61% . Finally, compared with seven states of the art classification algorithms, the proposed model achieves a higher prediction accuracy, which indicates that the results provide great potential to identify the poverty levels of college students.

Nowadays, poverty-stricken college students have become a special group among the college students and occupied higher proportion in it. How to accurately identify poverty levels of college students and provide funding is a new problem for universities. In this manuscript, a novel model that combined Random Forest with Principle Components Analysis (RF-PCA) is proposed prediction poverty levels of college students. To build this model, data was firstly collected to establish datasets including 4 classed of poverty levels and 21 features of poverty-stricken college students. Then, feature dimension reduction includes two steps: the first step we selected the top 16 features with the ranking of feature, according to the Gini importance and Shapley Additive explanations (SHAP) values of features based on Random Forest (RF); the second step of feature extraction through Principle Components Analysis (PCA) extracted 11 dimensions. Finally, confusion metrics and receiver operating characteristic (ROC) curves were used to evaluate the performance of the proposed model, the accuracy of the model achieved 78.61%. Furthermore, compared with seven different classification algorithms, the model has a higher prediction accuracy, the result has great potential to identify the poverty levels of college students.