Resource Scheduling Optimization Model in Cloud Computing Environment

Chun Yan Kang; Tie Jun Shi

doi:10.4028/www.scientific.net/AMM.687-691.1645

Paper Titles

Realization of RBF Neural Network with Local and Semi-Local Transfer Function Approximation and Classification
p.1628

The RBF Neural Network Learning Algorithm Based on Entropy Clustering and Competitive
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A Web Service Composition Algorithm Based on Graph Search and Semantic Web
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An Application of AHP and FAHP on the Model Prediction
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Resource Scheduling Optimization Model in Cloud Computing Environment
p.1645

Research of Dimensionality Reduction for Fatigue Stress Concentration Factor Based on SVM by Linear Kernel
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Sparse Coding in Modularity Community Detection with Big Data
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Fuzzy Maximum Independent Set Problem of Graphic
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The Differential Game Analysis on the Construction Problem of the Public Libraries
p.1662

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Resource Scheduling Optimization Model in Cloud...

Resource Scheduling Optimization Model in Cloud Computing Environment

Abstract:

In the process of cloud computing, the dynamic hierarchical resource index is researched, and the independent confusion cloud computing is studied. This problem has become the focus of data processing. Therefore, it needs to establish improved dynamic layered resource index independent confuse cloud computing model. According to the theory of support vector machine, all of the resources are taken with dynamical layered processing, different levels of resources are taken with the independent confusion cloud computing. The experiment results show that, this algorithm is taken for the dynamic layered resource cloud computing, calculation efficiency can be improved, computational complexity and redundancy are reduced, meet the practical demands of dynamic hierarchical resource index independent confused cloud computing. It has good application value in the cloud computing application.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

1645-1648

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.1645

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Online since:

November 2014

Authors:

Chun Yan Kang, Tie Jun Shi*

Keywords:

Dynamic Hierarchical, Independent Confusion, Loud Computing, Resources Index

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* - Corresponding Author

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