| In order to meet Service Level Agreement (SLA) requirements, efficient scaling of Virtual Machine (VM) resources in cloud computing needs to be provisioned for before actually required due to the instantiation time required by the VM. One way to proactively provision resources is by predicting future resource demands. Most existing research on VM resource provisioning are either reactive in their approach or use only non-business level metrics such as CPU, Memory and Network utilization in their prediction model. In this research, a Cloud client prediction model for TPC-W benchmark web application is developed and evaluated using three machine learning techniques: Support Vector Regression (SVR), Neural Networks (NN) and Linear Regression (LR). Business level metrics for Response Time and Throughput are included in the prediction model with the aim of providing cloud clients with a more robust scaling decision choice. Results and subsequent thorough analysis from the experimentation carried out on Amazon Elastic Compute Cloud (EC2) show that Support Vector Regression provides the best prediction model for random-like workload traffic pattern. |
