Nowadays, to meet the demands of Industry 4.0, a seamless convergence between the different cloud domains (edge, edge and cloud) is key for industries to have a cloud-native intelligent orchestration layer capable of establishing the necessary mechanisms to ensure continuity of services as data m

In industrial environments, cloud services and the Internet of Things (IoT) converge, with the need to manage complex and diverse computing environments for service and infrastructure providers, a process called cloud continuum.

In the rapidly evolving landscape of IoT-Edge-Cloud continuum (IECC), effective management of computational tasks offloaded from mobile devices to edge nodes is crucial.

The term ‘Dynamic Heterogeneous Computing Sphere’ is used to describe a computing paradigm that is heterogeneous, volatile and highly dynamic.

A proposal for a predictive analysis model for bike-sharing systems based on a mixed model of statistical and machine-learning approaches.The model assumes a Poisson distribution on bike station arrival and departures.