Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment
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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-09-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2073-8994/8/10/103 |
| _version_ | 1798039694885257216 |
|---|---|
| author | Sungju Lee Taikyeong Jeong |
| author_facet | Sungju Lee Taikyeong Jeong |
| author_sort | Sungju Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T21:57:18Z |
| format | Article |
| id | doaj.art-0b1ce74e5a0b48d5ab44c9e4458d5d81 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T21:57:18Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-0b1ce74e5a0b48d5ab44c9e4458d5d812022-12-22T04:01:03ZengMDPI AGSymmetry2073-89942016-09-0181010310.3390/sym8100103sym8100103Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise EnvironmentSungju Lee0Taikyeong Jeong1Department of Computer Information and Science, Korea University, Sejong 30019, KoreaDepartment of Computer Science and Engineering, Seoul Women’s University, Seoul 01797, KoreaA 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.http://www.mdpi.com/2073-8994/8/10/103cloud computing environmentsdata analysisstatistical analysisdata miningheterogeneous platformenterprise system |
| spellingShingle | Sungju Lee Taikyeong Jeong Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment Symmetry cloud computing environments data analysis statistical analysis data mining heterogeneous platform enterprise system |
| title | Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment |
| title_full | Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment |
| title_fullStr | Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment |
| title_full_unstemmed | Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment |
| title_short | Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment |
| title_sort | cloud based parameter driven statistical services and resource allocation in a heterogeneous platform on enterprise environment |
| topic | cloud computing environments data analysis statistical analysis data mining heterogeneous platform enterprise system |
| url | http://www.mdpi.com/2073-8994/8/10/103 |
| work_keys_str_mv | AT sungjulee cloudbasedparameterdrivenstatisticalservicesandresourceallocationinaheterogeneousplatformonenterpriseenvironment AT taikyeongjeong cloudbasedparameterdrivenstatisticalservicesandresourceallocationinaheterogeneousplatformonenterpriseenvironment |