One way to proactively provision resources and meet Service Level Agreements (SLA) is by predicting future resource demands a few minutes ahead because of Virtual Machine (VM) boot time. 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 have included two SLA metrics - Response Time and Throughput with the aim of providing the client with a more robust scaling decision choice. As an improvement to our previous work, we implemented our model on a public cloud infrastructure: Amazon EC2. Furthermore, we extended the experimentation time by over 200%. Finally, we have employed random workload pattern to reflect a more realistic simulation. Our results show that Support Vector Machine provides the best prediction model.

Additional Metadata
Keywords Cloud Computing, Machine Learning, Resourde Peovisioning, Resourde Prediction
Persistent URL dx.doi.org/10.1109/COMPSAC.2013.21
Conference 2013 IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013
Citation
Ajila, S, & Bankole, A.A. (Akindele A.). (2013). Cloud client prediction models using machine learning techniques. Presented at the 2013 IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013. doi:10.1109/COMPSAC.2013.21