doc. RNDr. Ing. Petr Zemánek, CSc.

The kernels of monolithic operating systems are currently probably the most complex centralized programs that undergo a process of continuous development and tuning of both functionality and performance. They have an architecture of hundreds of modules. A number of tools are currently available to diagnose the functionality and performance of these kernels, but the level of usability of the outputs of these tools for effective kernel debugging is highly dependent on the human factor, the maturity and experience of the development team.

The aim of the dissertation is to research methods of automatic detection of the causes of performance problems of the operating system kernel and kernel scalability for multiprocessor computer systems. Investigate which known techniques of artificial intelligence and machine learning can be used to automate these methods.