ABSTRACT
Data stream process on the cloud infrastructure run continuously with the varying load factors. Cloud infrastructure presents the system to meet the fluctuations on computational load. Cloud infrastructures meet the end to end latency objective and effectively predict the data streams of ensemble models. A different type of data stream supports the deep learning processing for all the workflows. Many data stream processing depends on work load balancing and operator scheduling on the secured cloud environment. Cloud computing uses the virtualized processing and storage resources in conjunction with modern technologies where it delivers the conceptual, scalable platforms and applications as on data services. Ensemble Tree Metric Space (E-tree MSI) analytics builds the system with deep learning process on the cloud infrastructure for the faster prediction of the results with effective balancing of load factor. Metric Space Indexing in E-tree MSI technique executes the classification operations on the cloud data streams. The stream applications analyze the temporal relation between secured data stream. The research work is carried out to perform the secured and fast prediction of the data for balancing the load. This technique improves the performance based on the factors such as CPU load rate, and system flexibility, prediction time, inorder searching result probability rate.
