| abstract |
Systems and methods are disclosed for using machine learning (e.g., neural networks and/or combinatorial learning) to solve the non-linear problem of predicting the provisioning of a server farm (e.g., cloud resources). The machine learning may be performed using commercially available products, such as the SNNS product from The University of Stuttgart of Germany. The system, which includes a neural network for machine learning, is provided with an identification of inputs and outputs to track, and the system provides correlations between those. Rather than static rules, the machine learning provides dynamic provisioning recommendations with corresponding confidence scores. Based on the data collected/measured by the neural network, the provisioning recommendations will change as well as the confidence scores. |