ENACT Project

Advance intelligent Cloud–Edge systems through graph-based modeling, reinforcement learning, and autonomous workload optimization.

 

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Adaptive Scheduling and Deployments of Data Intensive Workloads on Energy Efficient Edge to Cloud Continuum

Project Details

Funded under: Horizon Europe

Project ID: 101135423

Start Day: 01/01/2024 

Project Duration: 3 Years

Tags: Cognitive Computing Continuum, AI

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Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

The Story

As organizations increasingly deploy distributed applications across Cloud and Edge environments, they face growing complexity in managing dynamic resources while maintaining efficiency, reliability, and energy performance. Traditional infrastructures struggle to handle hyper-distributed, data-intensive workloads that require real-time adaptation and intelligent coordination.

The ENACT project addresses this challenge by enabling the next generation of the Cognitive Computing Continuum (CCC), a unified ecosystem that seamlessly integrates Cloud and Edge resources. Through simultaneous Infrastructure-level and Application-level adaptations, ENACT ensures optimal resource utilization while enabling applications to autonomously adjust their behavior in real time.

By combining AI-driven orchestration, advanced graph-based models, and zero-touch deployment mechanisms, ENACT empowers organizations to operate distributed systems that are intelligent, adaptive, and trustworthy.

Project Overview

ENACT is an EU-funded innovation project redefining how distributed applications are deployed and managed across heterogeneous Cloud and Edge infrastructures. At its core, the project develops an intelligent framework that governs both infrastructure orchestration and application behavior in a coordinated manner.

The project leverages advanced technologies such as Graph Neural Networks (GNNs) and Reinforcement Learning to enable intelligent resource allocation and workload distribution. In parallel, ENACT introduces a dedicated Application Programming Model (APM) and an Application Controller, allowing applications to dynamically self-configure based on performance, energy consumption, and operational conditions.

This dual-layer intelligence—spanning infrastructure and application logic—creates a fully adaptive Cognitive Computing Continuum capable of optimizing distributed workflows in real time. By integrating AI-driven orchestration with explainability and human oversight, ENACT ensures that distributed systems are not only efficient and scalable but also transparent and trustworthy.

Through this unified approach, ENACT equips European industries with the tools needed to manage complex distributed ecosystems automatically, enhancing competitiveness and sustainability.

Our Contribution

FDI plays a foundational role in the ENACT consortium by leading the definition of the project’s Reference Architecture and Specifications, designing the blueprint of the Cognitive Computing Continuum and ensuring alignment across all technical components.

FDI leads the architectural design of the CCC, ensuring that infrastructure orchestration, application adaptation, and AI-driven decision-making mechanisms operate cohesively within a unified framework.
FDI co-develops the AI Forecaster, utilizing Gated Recurrent Unit (GRU) networks to accurately predict real-time power consumption and network load across distributed environments.
The forecasting insights feed into an intelligent scheduler powered by Deep Reinforcement Learning (DRL), enabling optimized workload distribution across Cloud and Edge resources.
To maintain trust and human oversight, FDI develops Explainable AI (XAI) techniques for the GRU models, providing interpretable insights into resource allocation decisions within the autonomous orchestration framework.

The Results

ENACT establishes a fully adaptive Cognitive Computing Continuum for intelligent Cloud–Edge orchestration.

Real-time prediction of power consumption and network load
AI-driven workload optimization across distributed infrastructures
Autonomous application self-configuration for performance and energy efficiency
Transparent and explainable resource allocation decisions
A unified architectural blueprint for scalable, trustworthy distributed systems

Project’s Progress so far

70%

Consortium Partners

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