In order to meet Service Level Agreement (SLA) requirements, efficient scaling of Virtual Machine (VM) resources must be provisioned few minutes ahead due to the VM boot-up time. One way to proactively provision resources 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.

Additional Metadata
Keywords Cloud computing, Component, Machine learning, Resource prediction, Resource provisioning
Persistent URL dx.doi.org/10.1109/SOSE.2013.40
Conference 2013 IEEE 7th International Symposium on Service-Oriented System Engineering, SOSE 2013
Citation
Bankole, A.A. (Akindele A.), & Ajila, S. (2013). Cloud client prediction models for cloud resource provisioning in a multitier web application environment. Presented at the 2013 IEEE 7th International Symposium on Service-Oriented System Engineering, SOSE 2013. doi:10.1109/SOSE.2013.40