Jiménez-García, JorgeLacalle-Úbeda, IgnacioSzmeja, PawelWasielewska-Michniewska, KatarzynaHolda, PrzemyslawGanzha, MariaPalau Salvador, Carlos EnriqueBadica, CostinFidanova, StefkaPaprzycki, Marcin2026-07-232026-07-2320261820-0214https://riunet.upv.es/handle/10251/238259[EN] There is great potential in leveraging Artificial Intelligence (AI) systems to optimize complex infrastructures, automate difficult tasks, or support autonomy and coordination between networked devices. However, advances in state-of-theart AI often neglect features and/or requirements that businesses care deeply about, namely traceability and explainability. A majority of available research has not explored much the deployment of semi-physical architectures combining fuzzy rulebased systems with more opaque models to improve explainability, being this specially true for the management of microservices in cloud and cloud-edge environments. This contribution builds on previous work that proposes a middle ground of mixed AI architectures that combine the performance of black-box AI models with ¿a more explainable overall architecture¿ by implementing a microservice scaling system for distributed cloud environments using a cascade approach. This work demonstrates and evaluates an application case of such an approach departing from a Service Level Agreement compliance, in a case of microservice scaling decision over cloud (and cloud-like) infrastructures.Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)ExplainabilityFuzzy-rule-based systemsSemi-physical architecturesAI networkGray-boxArtículo10.2298/CSIS250425014GAbierto2196-9736