A fundamental key for enterprise users is a cloud-based parameter-driven statistical service\nand it has become a substantial impact on companies worldwide. In this paper, we demonstrate the\nstatistical analysis for some certain criteria that are related to data and applied to the cloud server\nfor a comparison of results. In addition, we present a statistical analysis and cloud-based resource\nallocation method for a heterogeneous platform environment by performing a data and information\nanalysis with consideration of the application workload and the server capacity, and subsequently\npropose a service prediction model using a polynomial regression model. In particular, our aim is to\nprovide stable service in a given large-scale enterprise cloud computing environment. The virtual\nmachines (VMs) for cloud-based services are assigned to each server with a special methodology\nto satisfy the uniform utilization distribution model. It is also implemented between users and\nthe platform, which is a main idea of our cloud computing system. Based on the experimental\nresults, we confirm that our prediction model can provide sufficient resources for statistical services\nto large-scale users while satisfying the uniform utilization distribution.
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