EMC INFORMATION SYSTEMS INTERNATIONAL UNLIMITED COMPANY

From edge to cloud, big data analytics is growing fast, and its energy consumption has become a reason of concern for national grids and they generate significant carbon emissions. The GLACIATION project aims to address this issue through energy-efficient privacy preserving data operations. By developing a novel Distributed Knowledge Graph (DKG) that stretches across the edge-core-cloud architecture, reduction in the energy consumption for data processing will be achieved through AI enforced minimal data movement operations. GLACIATION will achieve significant power consumption reduction through optimizing the location where analytics are carried out and where data is placed. The projects Metadata framework will provide tools that incorporate privacy and trust aspects in the data operations. GLACIATION is demonstrated on three relevant industry settings which benefit from optimized data movement and power consumption reduction. More specifically, GLACIATION use cases cover public-service, manufacturing, energy and enterprise data analytics.  

EC contribution:

EUR 1.1M

Start:

2022-10-01

End:

2025-09-30

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-CL4-2021-DATA-01

View on CORDIS (DOI 10.3030/101070141)

The main goal is to design an Extreme near-data platform to enable consumption, mining and processing of dis- tributed and federated data without needing to master the logistics of data access across heterogeneous data locations and pools. We go beyond traditional passive or bulk data ingested from storage systems towards next generation near-data processing platforms both in the Cloud and in the Edge. In our platform, Extreme Data in- cludes both metadata and trustworthy data connectors enabling advanced data management operations like data discovery, mining, and filtering from heterogeneous data sources. The three core objectives are: O-1 Provide high-performance near-data processing for Extreme Data Types: The first objective is to create a novel intermediary data service (XtremeDataHub) providing serverless data connectors that optimize data management operations (partitioning, filtering, transformation, aggregation) and interactive queries (search, discovery, matching, multi-object queries) to efficiently present data to analytics platforms. Our data connectors facilitate a elas- tic data-driven process-then-compute paradigm which significantly reduces data communication on the data interconnect, ultimately resulting in higher overall data throughput. O-2 Support real-time video streams but also event streams that must be ingested and processed very fast to Object Storage: The second objective is to seamlessly combine streaming and batch data processing for analytics. To this end, we will develop stream data connectors deployed as stream operators offering very fast stateful computations over low-latency event and video streams. O-3 The third objective is to create a Data Broker service enabling trustworthy data sharing and confidential orchestration of data pipelines across the Compute Continuum. We will provide secure data orchestration, transfer, processing and access thanks to Trusted Execution Environments (TEEs) and federated learning architectures.

EC contribution:

EUR 644k

Start:

2023-01-01

End:

2025-12-31

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-CL4-2022-DATA-01

View on CORDIS (DOI 10.3030/101092644)

Europe’s Digital Decade will deploy large fleets of Edge Datacenters (EDCs) by 2030, just as Europe’s electricity grid struggles to integrate variable renewables. The fundamental disconnect is that EDCs operate as passive loads: physically connected to the grid but blind to its real-time needs. This is due to two barriers: a lack of internal controls to safely coordinate fragmented subsystems (IT, cooling, batteries) to provide the verifiable performance the grid requires; and a lack of fleet coordination mechanisms, as energy markets require minimum bids that smaller EDCs cannot meet, with no trusted way to aggregate them into a unified resource. The main scientific objective of AEGIS is to transform EDCs from passive liabilities into grid-interactive assets by developing a Software-Defined Platform for EDC Flexibility. This platform is architected in three layers. The foundational cyber-physical layer provides runtime-verified control to unlock safe, quantifiable flexibility. The intelligence layer uses carbon-aware forecasting and control, enabling AI workloads to dynamically adapt their behavior and resource consumption in response to real-time grid signals. The orchestration layer enables trusted, fleet-scale operations using privacy-preserving mechanisms to create a unified grid resource. Our ambitious objectives require researchers with a unique combination of interdisciplinary and intersectoral skills. AEGIS’s 15 Doctoral Candidates will receive integrated training across key areas (computer science, power systems, control engineering, machine learning, and distributed systems) necessary to realize the potential of these technologies, moving between academic and industrial environments to bridge the gap between the energy and computing domains. AEGIS will provide a new generation of experts capable of driving future developments in sustainable, grid-interactive edge datacenters across Europe.

EC contribution:

EUR 341k

Start:

2026-10-01

End:

2030-09-30

Status:

SIGNED

Scheme:

HORIZON-TMA-MSCA-DN

Call:

HORIZON-MSCA-2025-DN-01

View on CORDIS (DOI 10.3030/101311399)

CLEVER proposes a series of innovations in the area of hardware accelerators, design stack, and middleware software that revolutionize the ability of edge computing platforms to operate federatedly, leveraging sparse resources that are coordinated to create a powerful swarm of resources. CLEVER technologies will support the deep edge computing paradigm, moving computing services closer to the end user or the source of the data to reduce power consumption, reduce capacity requirements, and latency for mission critical applications. Furthermore, CLEVER will overcome traditional limitations of edge computing in terms of limited resource availability by providing an effective framework for seamless use of federated resources in the edge-cloud continuum. CLEVER will demonstrate processing solutions for AI at the edge through four use cases: (1) digital twin for in-factory optimization, (2) smart agriculture for high yield eco-farms, (3) fully automated material deployment, and (4) augmented reality for shopping sites. Through the achievement of its goals, the CLEVER project will help to position Europe at the forefront of the intelligent edge computing field, enabling growth across many sectors (manufacturing, agriculture, smart environments, augmented reality, and others). By lowering the barriers for utilising edge computing for artificial intelligence applications, CLEVER will open the door for European Industries and SMEs to leverage state of the art technologies, driving their development and growth as leaders in their sectors.

EC contribution:

EUR 258k

Start:

2023-01-01

End:

2025-12-31

Status:

SIGNED

Scheme:

HORIZON-JU-RIA

Call:

HORIZON-KDT-JU-2021-2-RIA

View on CORDIS (DOI 10.3030/101097560)

The evolution of 6G wireless networks relies on advanced multiple-access technologies that improve efficiency and scalability. Reconfigurable Intelligent Surfaces (RIS) are set to transform 6G by enabling dynamic control of signal propagation channels. This project focuses on optimizing User Equipment (UE) pairings with Resource Blocks (RBs) and RISs in RIS-assisted Non-Orthogonal Multiple Access (NOMA) environments, exploring various configurations to maximize system throughput, fairness, and energy efficiency. By integrating active and passive beamforming, our approach not only enhances communication performance but also reduces the carbon footprint by minimizing the need for additional base stations through efficient frequency reuse. With Munster Technological University (MTU) affiliated with ADAPT Centre, and secondment at Dell Technologies, this research is strategically positioned for high-impact outcomes. The project introduces a novel technique for intelligent role switching in low mobility networks (LMNs), enabling dynamic shifts between near and far users to enhance connectivity time. The methodology incorporates optimization algorithms such as Simulated Annealing, Hill Climbing, Genetic Algorithms, and Random Walk for heuristic solutions. For LMNs, we will develop and evaluate models based on stochastic geometry and deep reinforcement learning (DRL) (asynchronous advantage actor-critic) to optimize the angle of reflection (AoR) for RIS elements. Four-month secondment at Dell, Ireland, is a key aspect where industry-grade validation will assess the performance of RO3 and refine the interplay between the edge-cloud continuum for the DRL. These models aim to address challenges in RIS-assisted networks, supporting seamless 6G connectivity in smart and sustainable cities. Ultimately, the research will advance green communications by optimizing network designs for energy-efficient infrastructure, aligning with EU goals for sustainable smarter cities.

EC contribution:

EUR 0

Start:

2027-01-04

End:

2029-01-03

Status:

SIGNED

Scheme:

HORIZON-TMA-MSCA-PF-EF

Call:

HORIZON-MSCA-2025-PF

View on CORDIS (DOI 10.3030/101273667)