The strong development of cloud computing and its massive adoption for the storage of unprecedented volumes of data in a growing number of domains has brought to light major technological challenges. In this project we will address several of these challenges, organized in three research directions:

• Exploitation of emerging technologies for efficient storage on cloud infrastructures. We will address this challenge through NVRAM-based distributed performance storage solutions, as close as possible to data production and consumption locations (disaggregation principle) and develop strategies to optimize the trade-off between data consistency and access performance.
• Efficient storage and processing of data on hybrid, heterogeneous infrastructures within the digital edge-cloud-supercomputer continuum. The execution of hybrid workflows combining simulations, analysis of sensor data flows and machine learning requires storage resources ranging from the edge to cloud infrastructures, and even to supercomputers, which poses challenges for unified data storage and processing.
• Confidential storage, in connection with the need to store and analyze large volumes of data of strategic interest or of a personal nature.