关键词: 云计算, 云工作流, 执行优化, 超参数优化

Abstract: With the maturity of cloud computing,increasing number of data analysis tasks are executed in cloud.Machine learning algorithm is a necessary part of data analysis,and the hyperparameter optimization for machine learning algorithms is a time-consuming and resource-consuming process.However,little research focuses on the final cost for executing hyperparameter optimization.For this reason,the method of executing the hyperparameter optimization as quick as possible with the lowest cost was studied on the basis of not changing the performance of learning model such as accuracy,precision,recall rate.An optimized workflow instance model was generated,which consisted of multiple parallel branches and each branch sequentially executed multiple models on a server.Based on the dual-colored bin packing algorithm,the branches were organized in such a way that they had a similar execution time and can be completed almost at the same time.Experimental results demonstrated that the proposed approach could meet the deadline and reduce the cost at the same time.

Key words: cloud computing, cloud workflow, execution optimization, hyperparameter optimization