Projektbeschreibung
Die technologischen Grenzen autonomer Systeme durch künstliche Intelligenz/Maschinenlernen verschieben
Innerhalb eines Datenverarbeitungskontinuums sind Edge- und Cloud-Computing notwendig, um zu gewährleisten, dass Anwendungen und Daten effizient verwaltet werden. Die Datensouveränität Europas und das Erreichen von Nachhaltigkeitszielen hängen direkt von Cloud- und Edge-Computing ab, da es sich bei ihnen um Schlüsseltechnologien handelt. Vor diesem Hintergrund wird das EU-finanzierte Projekt MLSysOps ein Rahmenwerk für ein autonomes Ende-zu-Ende-Systemmanagement im Cloud-Edge-IoT-Kontinuum auf der Grundlage von künstlicher Intelligenz/Maschinenlernen konzipieren und umsetzen. Zu erwarten ist, dass dieses Rahmenwerk die Effizienz der entscheidenden Ressourcenmanagement- und Anpassungsstrategien erheblich verbessern wird. Das Projekt wird sein Rahmenwerk in realen Testumgebungen in den Bereichen intelligente Städte und intelligente Landwirtschaft erproben.
Ziel
MLSysOps will achieve substantial research contributions in the realm of AI-based system adaptation across the cloud-edge continuum by introducing advanced methods and tools to enable optimal system management and application deployment. MLSysOps will design, implement and evaluate a complete framework for autonomic end-to-end system management across the full cloud-edge continuum. MLSysOps will employ a hierarchical agent-based AI architecture to interface with the underlying resource management and application deployment/orchestration mechanisms of the continuum. Adaptivity will be achieved through continual ML model learning in conjunction with intelligent retraining concurrently to application execution, while openness and extensibility will be supported through explainable ML methods and an API for pluggable ML models. Flexible/efficient application execution on heterogeneous infrastructures and nodes will be enabled through innovative portable container-based technology. Energy efficiency, performance, low latency, efficient, resilient and trusted tier-less storage, cross-layer orchestration including resource-constrained devices, resilience to imperfections of physical networks, trust and security, are key elements of MLSysOps addressed using ML models. The framework architecture disassociates management from control and seamlessly interfaces with popular control frameworks for different layers of the continuum. The framework will be evaluated using research testbeds as well as two real-world application-specific testbeds in the domain of smart cities and smart agriculture, which will also be used to collect the system-level data necessary to train and validate the ML models, while realistic system simulators will be used to conduct scale-out experiments. The MLSysOps consortium is a balanced blend of academic/research and industry/SME partners, bringing together the necessary scientific and technological skills to ensure successful implementation and impact.
Wissenschaftliches Gebiet
- natural sciencescomputer and information sciencesinternetinternet of things
- natural sciencescomputer and information sciencessoftwaresoftware applicationssystem software
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencesdata sciencedata processing
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Schlüsselbegriffe
Programm/Programme
Thema/Themen
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenFinanzierungsplan
HORIZON-RIA - HORIZON Research and Innovation ActionsKoordinator
38221 Volos
Griechenland