Use Machine Learning to Predict the Running Time of the Program

The prediction of program running time can be used to improve scheduling performance of distributed systems. In 2011, Google released a data set documenting the vast amount of information in the Google cluster. However, most of the existing running time prediction models only consider the coarse-grained characteristics of the running environment without considering the influence of the time series data of the running environment on the prediction results. Based on this, this paper innovatively proposes a model to predict the running time of the program, which predicts the future running time through historical information. At the same time, we also propose a new data processing and feature extraction scheme for Google cluster data sets. The results show that our model greatly outperforms the classical model on the Google cluster data set, and the root-mean-square error index of running time under different prediction modes is reduced by more than 60% and 40%, respectively. We hope that the model proposed in this paper can provide new research ideas for cloud computing system design.