The ANTILLAS project in particular has two research focuses. On the one side monitoring and control of system parameters of network components. The dynamic and automated (re-)allocation of hardware resources requires adaptive (network) operating-system components. A prerequisite therefore is a comprehensive system monitoring (e.g., load, bandwith allocation, latency, power demand). Machine learning, for example deep neural networks, are a powerful tool to analyse the incoming monitoring data and make fast and anticipatory decisions to improve functional and non-functional properties of the system.
On the other side new hardware technologies like non-volatile memory technologies enable novel implementation paradigms and systems, which can accomplish, for example, better availability guarantees and more efficient runtime properties (e.g., latency, power demand). However, such systems require fundamentally new operating-system components to ensure data integrity and consistency due to the persistency of the underlying main-memory technology.