Secure and efficient data storage and processing on cloud-based infrastructures
— Preview
The STEEL project aims to provide solutions for efficient and secure data storage and processing on cloud-based infrastructures.
Gabriel Antoniu, Inria Research Director
Keywords : Cloud, data storage and processing, high-performance storage, confidential storage, hybrid cloud-edge infrastructures
The strong development of cloud computing since its emergence, and its massive adoption for storing unprecedented volumes of data in a growing number of fields, has brought to light some major technological challenges. In this project, we will address several of these challenges, organized into three axes:
- The first involves exploiting emerging technologies for high-performance storage on cloud infrastructures, notably NVRAM-based, close to where data is produced and consumed (disaggregation principle).
- The second area concerns the efficient storage and processing of data on hybrid, heterogeneous infrastructures within the digital edge-cloud-supercomputer continuum. This responds to the emergence of hybrid workflows combining simulations, analysis of sensor data flows and machine learning in many fields (autonomous cars, predictive maintenance, intelligent buildings, etc.). Their execution requires adequate management of storage resources, from the edge to cloud infrastructures, to enable them to be processed in a unified framework.
- The third axis is dedicated to confidential storage, in line with the need to store and analyze large volumes of data of strategic interest or of a personal nature.
— Missions
— Our researches
STEEL is organized around three technical work packages. A fourth work package is dedicated to management, communication and dissemination of results.
— Partners
Consortium
Inria, IMT, CNRS, Université de Bordeaux, Université Grenoble Alpes, Université de Rennes, INSA Rennes, INSA Lyon, IMT Atlantique
— Research team
16 permanent staff (not funded by the project)
17 people funded by the project, including:
8 PhD students
2 post-docs
6 engineers
1 contract researcher
Our teams in France
— Publications
Journal articles
- Aghiles Ait Messaoud, Sonia Ben Mokhtar, Anthony Simonet-Boulogne. Tee-based key-value stores: a survey. The VLDB Journal, 2024, 34 (1), pp.10. ⟨10.1007/s00778-024-00877-6⟩. ⟨hal-04846840⟩
- Cédric Prigent, Alexandru Costan, Gabriel Antoniu, Loïc Cudennec. Enabling Federated Learning across the Computing Continuum: Systems, Challenges and Future Directions. Future Generation Computer Systems, 2024, 160, pp.767-783. ⟨10.1016/j.future.2024.06.043⟩. ⟨hal-04659211⟩
Conference papers
- Cédric Prigent, Kate Keahey, Alexandru Costan, Loïc Cudennec, Gabriel Antoniu. On the Reproducibility Challenges of Federated Learning: Investigating the Gap between Simulation, Emulation and Real-World Deployments. CCGrid 2025 - IEEE 25th International Symposium on Cluster, Cloud and Internet Computing, May 2025, Tromso, Norway. pp.1-11. ⟨hal-04997547⟩
- Aghiles Ait Messaoud, Sonia Ben Mokhtar, Anthony Simonet-Boulogne. TruShare: Confidential Key-Value Store for Untrusted Environments. 20th European Dependable Computing Conference (EDCC '25), Apr 2025, Lisbon, Portugal. ⟨hal-04935076⟩
- Robin Boëzennec, Danilo Carastan-Santos, Fanny Dufossé, Guillaume Pallez. Allocation Strategies for Disaggregated Memory in HPC Systems. HiPC 2024 - 31st IEEE International Conference on High Performance Computing, Data, and Analytics, Dec 2024, Bengalore, India. pp.1-11. ⟨hal-04815672⟩
- Cédric Prigent, Melvin Chelli, Alexandru Costan, Loïc Cudennec, René Schubotz, et al.. Efficient Resource-Constrained Federated Learning Clustering with Local Data Compression on the Edge-to-Cloud Continuum. HiPC 2024 - 31st IEEE International Conference on High Performance Computing, Data, and Analytics, Dec 2024, Bengaluru (Bangalore), India. pp.1-11, ⟨10.1109/HiPC62374.2024.00033⟩. ⟨hal-04779813⟩
- Mathis Valli, Alexandru Costan, Cédric Tedeschi, Loïc Cudennec. Towards Efficient Learning on the Computing Continuum: Advancing Dynamic Adaptation of Federated Learning. FlexScience 2024 - 14th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures, Jun 2024, Pisa, Italy. pp.42-49, ⟨10.1145/3659995.3660042⟩. ⟨hal-04698619v2⟩
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