Predicting cloud resource provisioning using machine learning techniques

In order to meet Service Level Agreement (SLA) requirements, Virtual Machine (VM) resources must be provisioned few minutes ahead due to the VM boot-up time. One way to do this is by predicting future resource demands. In this research, we have developed and evaluated cloud client prediction models for TPCW benchmark web application using three machine learning techniques: Support Vector Machine (SVM), Neural Networks (NN) and Linear Regression (LR). We included the SLA metrics for Response Time and Throughput to the prediction model with the aim of providing the client with a more robust scaling decision choice. Our results show that Support Vector Machine provides the best prediction model.