Novel concept of advanced adaptation of cloud resource using predicted demand for Cloud resources. Presented approach is using advanced forecasting methods combined with machine learning based solvers, which can dynamically adapt to changed workload. The benefits of that approach will be shown.
The presentation, with the practical examples of the novel approach to proactive optimization of cloud resources based on dynamical and anticipated use of resources. The prediction of application workload is provided as input to the advanced, machine learning based solvers which calculate the optimal deployment plan for the application to anticipate the future needs. The latest state of the art methods are used for forecasting, like ES-Hybrid and advanced Monte Carlo Tree Search based solvers are used to find the optimal solution.
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