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 TPC-W 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.
|Keywords||Cloud computing, Machine learning, Resource prediction, Resource provisioning|
|Conference||2013 26th IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2013|
Bankole, A.A. (Akindele A.), & Ajila, S. (2013). Predicting cloud resource provisioning using machine learning techniques. Presented at the 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2013. doi:10.1109/CCECE.2013.6567848