Abstract: Big data and stream data computing have been widely used to support scenarios such as anomaly detection and early warning in smart grids.Cloud computing serves as the mainstream operating environment for big data and stream data applications.However,optimizing performance by selecting suitable cloud resources poses significant challenges.Current methods based on exhaustive configuration searches use all candidate cloud configurations as the search space,leading to excessively large search spaces and have the risk of getting stuck in local optima.To address this issue,this paper proposes a resource preference-sensitive cloud configuration recommendation method for big data applications.It employs a resource preference-sensitive random forest model as the probabilistic model in Bayesian optimization to balance the accuracy and cost of searches when the configuration option space is large.Experimental results show that,compared to the exhaustive configuration search method CherryPick,the proposed method improves search accuracy by 23% while reducing the number of searches by 25%~44%.Compared to the data-driven method RP-CH,the accuracy of search results is 10% lower,but the average number of searches is effectively reduced by 78%.

Key words: Big data applicaiton, Cloud configuration recommendation, Resource preference, PCA, Bayesian optimization