GLACIATION GLACIATION is a three-years long project funded by the European Union flagship research and innovation programme Horizon Europe.

The project consortium consists of 15 organisations leaders in computer engineering, smart manufacturing, public policy, technological development, innovation management, business information system security and public administrations.

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GLACIATION stems from a thorough assessment of the current state of edge storage optimizations. The data ecosystem matures towards the edge where an increasing volume of data is created, collected, and shared by and between a growing number of users through heterogenous devices and applications.

Energy consumption, latency, and reliability will push data operations even more at the edge of the ecosystem. However, current storage, energy, and analytics optimisation approaches are focused on a cloud-based infrastructure.



GLACIATION consortium proposes a solution to address the limitations and improve the energy, cost, and efficiency of storage optimization.

The solution proposed is an energy-aware storage optimization approach which combines relevancy and resource elasticity to optimize data placement in a distributed edge infrastructure.

The approach considers the data lifecycle and utilizes the relevancy of the data to ensure efficient and cost-effective data movement while still meeting the security, privacy, latency and reliability requirements of the edge applications.

GLACIATION is developing a platform that reduces energy consumption for data processing and analytics through AI-enforced minimal data movement operations.

This platform will enable organizations to deploy and manage analytics across the edge-core-cloud continuum in a secure, energy efficient, and simple manner. This is made possible by a Distributed Knowledge Graph (DKG) that spans across the edge-core-cloud continuum.



GLACIATION platform will allow for energy-efficient distributed flow of data from the edge to the cloud.

The AI-powered workflow allocation enhances energy-efficiency satisfying the computation needs. The AI engine handles the dynamically changing flow of data across the edge-core-cloud continuum for continuous training and optimisation of resources usage and workflow allocation.

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Swarm technology allows to coordinate the applications at the edge for an optimal functioning of the GLACIATION platform with a data-centric approach.

Built-in intelligence will be needed to tackle the specific challenges of a data-centric approach relating to scalability, energy and bandwidth efficiency, dependability/trustworthiness, adaptability and transparency.