Descripción del proyecto
Superación de los límites tecnológicos de los sistemas autónomos mediante inteligencia artificial y aprendizaje automático
La computación periférica y en nube son necesarias en el continuo informático para garantizar que las aplicaciones y los datos se gestionan de forma eficiente. La soberanía de los datos de Europa y la consecución de los objetivos de sostenibilidad dependen de la computación en nube y periférica como facilitadores tecnológicos fundamentales. Teniendo esto en cuenta, el equipo del proyecto MLSysOps financiado con fondos europeos, diseñará y aplicará un marco para la gestión autónoma de sistemas de extremo a extremo en el continuo nube-borde-internet de las cosas basado en la inteligencia artificial y el aprendizaje automático. Se espera que este marco mejore significativamente la eficiencia en la gestión de recursos cruciales y las estrategias de adaptación. En el proyecto se pondrá a prueba su marco en bancos de pruebas reales en los ámbitos de las ciudades y la agricultura inteligentes.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
Régimen de financiación
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinador
38221 Volos
Grecia