ONOFRE3-UPCT. Adaptación de Recursos de Cómputo y Red desde la Nube al Extremo: Planificación y acceso coordinado óptimo. PI - E. Egea López, J. Santa Lozano

The project ONOFRE-3 deals with a 5G/6G ecosystem featured by the heterogeneity of the edge, fog, and cloud processing layers for proper management of dynamic QoS application requirements running on mobile nodes. To overcome this complexity, AI and Machine Learning techniques for contextual information prediction and network management are proposed. AI/ML can also support both offline planning methods and multiaccess coordination and control and be deployed at end devices. In this scenario, security is also mandatory from the start, hence, advanced cloud data privacy and security techniques are proposed to be smoothly managed and controlled across different cloud computing domains. Finally, practical evaluation of these mechanisms needs a realistic application scenario with clear requirements. In this line, CCAM and C-ITS verticals are taken for reference evaluation, which provide clearly defined and demanding QoS requirements and serve as reference area to analyse and develop concrete and meaningful benchmarks in a massive latency-bounded slice.