This paper proposed an approach to mathematical modeling of the file system performance in a hypervisor-based virtual environment, with special focus on the file system pair interactions. The main goal of this research is to conduct an in-depth analysis of the filesystem pair behavior with respect to the performance costs originating from the employed technologies, such as H-Trees, B-Trees and Copy-on-Write/Overwrite update method, and different application workload types. The modeling provides a collection of hypotheses about the expected behavior. The modeling and the hypotheses are validated based on the results obtained for a specific case study. Our study reports on a file system performance comparison in the context of KVM hypervisor-based full hardware virtualization, application-level benchmarking, and 64-bit Linux filesystems Ext4, XFS, and Btrfs. The Filebench benchmark tool is applied for comprehensive testing of the filesystem performance under fair-play conditions. According to the obtained results, we provide a set of recommendations (i.e., a Knowledge Data Base) for optimal filesystem pair selection for the KVM hypervisor. Finally, it is important to note that the proposed modeling is also applicable to other hypervisor-based virtualizations.

This paper proposes an approach to evaluate spoken words based on lip movements without accompanying sound signals using convolutional neural networks. The main goal of this research is to prove the efficiency of neural networks in the field, where all data is received from an array of images. The modeling and the hypotheses are validated based on the results obtained for a specific case study. Our study reports on speech recognition from only a sequence of images provided, where all crucial data and features are extracted, processed, and used in a model to create artificial consciousness.