|Table of Contents|

Load forecasting model of cloud computing resourcesbased on support vector machine(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年06期
Page:
687-
Research Field:
Publishing date:

Info

Title:
Load forecasting model of cloud computing resourcesbased on support vector machine
Author(s):
Zhao Li
College of Information Engineering,Xinyang Agriculture and Forestry University,Xinyang 464000,China
Keywords:
support vector machine cloud computing resources load prediction chaotic analysis algorithm combined kernel function
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2018.42.06.008
Abstract:
A cloud computing resource load forecasting model is designed to describe the dynamic change trend of a cloud computing resource load accurately. The load time sequence of the cloud computing resource is handled by the chaotic analysis algorithm,and learning samples of cloud computing resource load forecasting is constructed. A cloud computing source load forecasting model is established by the support vector machine(SVM),and a combination kernel function is designed to improve the learning ability of the SVM. The simulation comparison experiments for cloud computing resource load forecasting are carried out comparing with the SVM of the gray model,the back propagation(BP)neural network,the radial basis function(RBF)neural network and the RBF kernel function. The results show that the accuracy of this model is 94.85% for single step cloud computing resource load forecasting,and is lower than that of the gray model(95.85%)only; the accuracy of this model is 89.17% for multi-step cloud computing resource load forecasting,and is the highest.

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Last Update: 2018-12-30