Imagine a giant brain that manages all your computers and servers across different locations. Instead of a human guessing where to run a program, this system uses a smart map to see which server has the most energy and space. It automatically moves tasks around to keep things running fast while using the least amount of electricity.
Companies struggle to manage data-intensive apps across mixed cloud and edge environments, leading to wasted energy and inefficient resource use. Manually configuring these distributed systems is complex and slow.
An intelligent decision-making engine using GNN and DRL, an Application Programming Model (APM), and a corresponding SDK for platform-agnostic app development.
If you are a traffic management provider dealing with massive data from thousands of street sensors — this project developed an intelligent decision-making engine that optimizes where data is processed. This ensures real-time traffic updates without overloading local servers or wasting energy.
If you are a factory operator dealing with fragmented edge devices and high energy costs — this project developed an SDK and programming model that lets apps self-determine their best deployment. This reduces manual configuration and improves energy efficiency across the production line.
If you are a logistics firm dealing with hyper-distributed data across ships and ports — this project developed Dynamic Graph Models to visualize connectivity and dependencies. This allows for predictive management of resources to keep tracking systems online and efficient.
Based on available project data, there is no specific pricing or commercial cost mentioned; the project is funded by an EU contribution of EUR 5,055,074.
Yes, the project is designed for hyper-distributed applications and will be validated in 3 use-cases with challenging resource requirements to ensure scalability.
Based on available project data, specific licensing terms are not provided, but the project aims to replace scheduling components in open-source solutions like KubeEdge.
It integrates by replacing the scheduling component of open-source tools like KubeEdge and providing an SDK for developing platform-agnostic applications.
The project period runs from 2024-01-01 to 2026-12-31, suggesting the final validated solutions will be ready by the end of 2026.
The consortium is heavily weighted toward commercial application, with 13 industry partners (72% of the total) and 9 SMEs. This high industry ratio, combined with a diverse geographic spread across 9 countries, indicates a strong focus on market-ready outcomes rather than pure academic research.
Contact ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS in Greece
Contact us to connect with the ENACT consortium for early adoption of the SDK.
