Experimental design of automatic virtual machine configuration based on applicative approach

The present paper proposes an analysis of the experimental verification results of the means of automatic determining of the optimal configuration of a virtual machine (VM) implemented based on previously developed models and methods of automation for virtual machine configurations (including in the conditions of changing loads). For information process analytical models, machine learning algorithms with reinforcements are applied. All the while, models are constructed automatically in the language of the typed π-calculus taking into account the journal entries of the functions performed by the VM. In order to calculate the optimal configuration of the VM, a machine learning Q-algorithm has been implemented. Its special feature is the reduction of terms correspondent to information processes on the basis of an abstract machine with states. This being said, the implemented method for modeling information processes performed by the VM uses an applicative approach in the form of an abstract machine.