Title: Resource optimisation and fault detection algorithms for cloud computing platforms based on SVM and resource reserve strategy
Authors: Xilong Qu; Srikanta Patnaik
Addresses: School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, Hunan, 410205, China ' School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, Hunan, 410205, China
Abstract: Efficient operation of cloud computing platforms depends on the optimised virtual resources and faster fault diagnosis system of the virtual machines. This paper proposes an algorithm by introducing a virtual machines based on elastic reservation mechanism, which can improve the availability of cloud resources through the demand analysis taking the help of support vector machines which has advantages resolving nonlinear and high dimensional classification problems. Secondly it adopts the anomaly detection algorithm based on support vector machines for failure analysis. In addition, the dimensionality problem can be sorted out by means of principal component analysis (PCA) algorithm and a kernel function used for distance measurement. It establishes the topological structure for the image set of feature space with Delaunay triangulation and analyses the relationship between kernel parameter and regulator, in order to build an effective model.
Keywords: virtual machine; cloud platform; support vector machine; SVM; principal component analysis; PCA; Delaunay triangulation.
International Journal of Applied Decision Sciences, 2018 Vol.11 No.3, pp.223 - 237
Available online: 05 Apr 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article