Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment

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2016
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Abstract
A fundamental key for enterprise users is a cloud-based parameter-driven statistical service and it has become a substantial impact on companies worldwide. In this paper, we demonstrate the statistical analysis for some certain criteria that are related to data and applied to the cloud server for a comparison of results. In addition, we present a statistical analysis and cloud-based resource allocation method for a heterogeneous platform environment by performing a data and information analysis with consideration of the application workload and the server capacity, and subsequently propose a service prediction model using a polynomial regression model. In particular, our aim is to provide stable service in a given large-scale enterprise cloud computing environment. The virtual machines (VMs) for cloud-based services are assigned to each server with a special methodology to satisfy the uniform utilization distribution model. It is also implemented between users and the platform, which is a main idea of our cloud computing system. Based on the experimental results, we confirm that our prediction model can provide sufficient resources for statistical services to large-scale users while satisfying the uniform utilization distribution.
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lee2016cloudbasedsymmetry Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sungju Lee, Taikyeong Jeong
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Journal Symmetry
Year 2016
DOI
DOI not found
URL
http://www.mdpi.com/2073-8994/8/10/103
Keywords
Medicine Engineering (General). Civil engineering (General) Information technology Technology Science (General) Science chemical engineering management. industrial management telecommunication applied optics. photonics communication. mass media mathematics electronic computers. computer science geology geography. anthropology. recreation cybernetics information theory Data mining Statistical Analysis data analysis

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