Project description
Navigating the future of distributed computing
The demand for efficient data processing has surged in modern computing. However, traditional infrastructures face challenges in meeting the intensive requirements of diverse applications, from complex simulations to real-time analytics. What complicates matters is the varied nature of computing resources across the Cloud, Edge, and IoT domains, each presenting unique challenges and opportunities. In this context, the EU-funded HYPER-AI project aims to redefine data processing by harnessing distributed computing swarms. These are autonomous networks of interconnected nodes that dynamically adapt and optimise resources throughout the computing continuum. By implementing semantic representation and autonomic principles, HYPER-AI promises enhanced self-configuring, self-healing, and self-optimising capabilities.
Objective
In HYPER-AI, we work with smart virtual computing entities (nodes) that come from a variety of infrastructures that span all three of the so-called computing continuum's layers: the Cloud, the Edge, and IoT.
It focuses on intensive data-processing applications that present the potential to improve their footprint when hyper-distributed in an optimized manner. In order to give targeted applications access to computational, storage, or network services, HYPER-AI implements the idea of computing swarms as autonomous, self-organized, and opportunistic networks of smart nodes. These networks may offer a diverse and heterogeneous set of resources processing, storage, data, communication) at all levels and have the ability to dynamically connect, interact, and cooperate.
HYPER-AI proposes semantic representation concepts to enable heterogeneous resources’ abstraction in a homogeneous way, under a common annotation (computing node), across the whole range of network infrastructures. The main orchestration and control concept of HYPER-AI is inspired by autonomic systems (self-CHOP principles) which employ swarmed computing schemes. Its objective is to make smart multi-node (swarm) deployment scenario design, execution, and monitoring easier, through appropriate AIs for self-configuration (nodes assigned resources), self-healing (swarmed nodes lifecycle), self-optimizing (exploiting built-in situation awareness mechanisms) and self-protecting (intrusion detection, privacy, security, encryption and identity management) at application runtime. In order to support dynamic and data-driven application workflows, HYPER-AI suggests the flexible integration of resources at the edge, the core cloud, and along the big data processing and communication channel, enabling their energy, time and cost-efficient execution. Finally, distributed ledger concepts for security, privacy, and encryption as well as AI-based intrusion detection are also considered.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
57001 Thermi Thessaloniki
Greece