GLACIATION: Green responsibLe privACy preservIng dAta operaTIONs

The rapid maturation of edge technologies coincides with an escalating deployment of distributed applications handling dynamically changing, heterogeneous data across a growing number of users, devices, and infrastructures [1]. This trend, while enabling new capabilities, presents signifi cant challenges, particularly concerning the energy consumption associated with large-scale data analytics and the imperative to ensure privacy and security in distributed data operations [2, 3]. Current optimization strategies, often rooted in traditional cloud paradigms, prove insuffi cient for the complexities of the edge-core-cloud continuum, failing to adequately address effi cient data movement, placement, and lifecycle management [1, 4].GLACIATION addresses these critical challenges by developing an innovative platform designed to enhance the effi ciency and trustworthiness of digital technologies for data operations spanning the edge-core-cloud architecture [1, 5]. Central to the project is the creation of a novel Distributed Knowledge Graph (DKG) that provides a semantic representation of the entire ecosystem [1, 2]. Leveraging this DKG, Artifi cial Intelligence (AI) techniques are employed to enforce minimal data movement and optimize the placement of analytics workloads, thereby signifi cantly reducing energy consumption while upholding stringent privacy and confi dentiality requirements demanded by citizens, industry, and public administrations [3, 4].