Using Machine Learning Algorithms for Cloud Client Prediction Models in a Web VM Resource Provisioning Environment

  • Samuel Adesoye Ajila Department of Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa K1S 5B6, ON Canada,
  • Akindele A. Bankole Department of Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa K1S 5B6, ON Canada,
Keywords: Cloud Computing, Resource Provisioning, Prediction, Machine Learning, Support Vector Machine, Neural Network, Linear Regression


In order to meet Service Level Agreement (SLA) requirements, efficient scaling of Virtual Machine (VM) resources in cloud computing needs to be provisioned  ahead due to the instantiation time required by the VM. One way to do this is by predicting future resource demands. The existing research on VM resource provisioning are either reactive in their approach or use only non-business level metrics. 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 analysis from the experiments carried out on Amazon Elastic Compute Cloud (EC2) show that Support Vector Regression provides the best prediction model for random-like workload traffic pattern.

Author Biography

Samuel Adesoye Ajila, Department of Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa K1S 5B6, ON Canada,
Systems and Computer Engineering, Associate Professor


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