HYPER-AI edit edit source
Hyper-Distributed Artificial Intelligence Platform for Network Resources Automation and Management Towards More Efficient Data Processing Applications edit edit source
Full project details (EU Research results portal): https://cordis.europa.eu/project/id/101135982
Project description: edit edit source
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.
EuroVoc IDs: /natural sciences/computer and information sciences/internet/internet of things EU Programme: Horizon Europe
Relevant IPCEI-CIS Reference Architecture components: edit edit source
Management, Logging, Monitoring and Alerting, Performance Management, Fault Management, Catalog/Repository Management, Operation Automation, Security and compliance Identity and Access Management, Identity Management, Key Management Service, Audit Log, Service Compliance Verification, Sustainability, Application layer, Data layer, Data Pipelines, Data Modelling, Data Exposure, Data Policy Control, Data Federation, AI Layer Cloud-Edge Training, Cloud-Edge Inference, Federated Learning, Service orchestration, Service Orchestrator, Cloud Edge Platform Physical Infrastructure Manager, Virtual Infrastructure Platform Manager, Cloud Edge Access Control, Cloud Edge Resource Repository, Serverless Orchestrator (FaaS), Virtualization Virtual Infrastructure Manager (IaaS), Physical Cloud Edge Resources, Physical Network Resources
EU Project