Neural network and regression based processor load prediction for efficient scaling of Grid and Cloud resources

An effective and efficient resource allocation policy could benefit the cloud environment by saving cost. To support the continuous load increase, the cloud platform needs to create new virtual machines. However, substantial amount of time is required for the creation and the setup of a virtual machine. Therefore, allocating resources in advance based on prediction models could improve the quality of the service of the cloud platform. In this paper we present time delay neural network and regression methods for predicting future workload in the Grid or Cloud platform. We use real world workload traces to test the performance of our algorithms. We also present an overall evaluation of this approach and its potential benefits for enabling efficient auto-scaling of Cloud user resources.