Summary
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.
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.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101135982 |
| Start date: | 01-04-2024 |
| End date: | 31-03-2027 |
| Total budget - Public funding: | - 4 628 975,00 Euro |
Cordis data
Original description
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.
Status
SIGNEDCall topic
HORIZON-CL4-2023-DATA-01-04Update Date
25-12-2024
Images
No images available.
Geographical location(s)
Structured mapping
Unfold all
/
Fold all
Search
Organisations
-
| ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS (CERTH) (Coördinator)
-
| COMMPLA SRL
-
| Centre Suisse d'Electronique et de Microtechnique SA
-
| EBOS TECHNOLOGIES LIMITED
-
| ECLIPSE FOUNDATION EUROPE GMBH (ECL)
-
| ENEA - Italian National Agency for New Technologies, Energy and Sustainable Economic Development
-
| ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON
-
| HAUTE ECOLE SPECIALISEE DE SUISSE OCCIDENTALE
-
| Kompetenzzentrum das Virtuelle Fahrzeug Forschungsgesellschaft mbH (Virtual Vehicle)
-
| ODIN SOLUTIONS SOCIEDAD LIMITADA
-
| SABO - INDUSTRIAL AND COMMERCIAL INCORPORATED COMPANY OF MACHINERY FOR THE BRICK AND TILE INDUSTRY A...
-
| SUNDOSOFT LTD
-
| TECHNOLOGIKO PANEPISTIMIO KYPROU
-
| TELEFONICA INNOVACION DIGITAL SL
-
| TRUST-IT SRL