Journal ArticleDOI
A Prediction based Cloud Resource Provisioning using SVM
Vijayasherly Velayutham,Srimathi Chandrasekaran +1 more
- Vol. 13, Iss: 3, pp 531-535
TLDR
The proposed model incurred a reduction in the training time, Root Mean Square Error and a marginal increase in the R2 Score than the traditional SVM, and was found to increase upon running the same set of tasks through the proposed model.Abstract:
To develop a prediction model grounded on Machine Learning using Support Vector
Machine (SVM).
Prediction of workload in a Cloud Environment is one of the primary task in provisioning
resources. Forecasting the requirements of future workload lies in the competency of predicting
technique which could maximize the usage of resources in a cloud computing environment.
To reduce the training time of SVM model.
K-Means clustering is applied on the training dataset to form ‘n’ clusters firstly. Then, for
every tuple in the cluster, the tuple’s class label is compared with the tuple’s cluster label. If the two
labels are identical then the tuple is rightly classified and such a tuple would not contribute much
during the SVM training process that formulates the separating hyperplane with lowest generalization
error. Otherwise the tuple is added to the reduced training dataset. This selective addition of tuples
to train SVM is carried for all clusters. The support vectors are a few among the samples in reduced
training dataset that determines the optimal separating hyperplane.
On Google Cluster Trace dataset, the proposed model incurred a reduction in the training
time, Root Mean Square Error and a marginal increase in the R2 Score than the traditional SVM.
The model has also been tested on Los Alamos National Laboratory’s Mustang and Trinity cluster
traces.
The Cloudsim’s CPU utilization (VM and Cloudlet utilization) was measured and it
was found to increase upon running the same set of tasks through our proposed model.
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References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Fast training of support vector machines using sequential minimal optimization, advances in kernel methods
TL;DR: SMO breaks this large quadratic programming problem into a series of smallest possible QP problems, which avoids using a time-consuming numerical QP optimization as an inner loop and hence SMO is fastest for linear SVMs and sparse data sets.
Book
Fast training of support vector machines using sequential minimal optimization
TL;DR: In this article, the authors proposed a new algorithm for training Support Vector Machines (SVM) called SMO (Sequential Minimal Optimization), which breaks this large QP problem into a series of smallest possible QP problems.