The modern computing scenario of the Computing Continuum exhibits large and complex
applications with heterogeneous requirements running on distributed infrastructure. Still …

The Internet of Things (IoT) has led to a surge in smart devices, generating vast volumes of
data. Cloud computing offers scalability but does not suffice for many real-time and privacy …

In the context of autonomous vehicles (AVs), offloading is essential for guaranteeing the
execution of perception tasks, eg, mobile map** or object detection. While existing work …

The rapid evolution of cloud-native technologies has facilitated seamless application
deployment and execution across the entire edge-to-cloud continuum. This continuum offers …

The increasing volume and complexity of IoT systems demand a transition from the cloud-
centric model to a decentralized IoT architecture in the so called Computing Continuum, with …

With the growth of real-time applications and IoT devices, computation is moving from cloud-
based services to the low latency edge, creating a computing continuum. This continuum …

Edge computing provides critical resources for various latency-sensitive applications,
including, safety-critical monitoring systems that process large volumes of data from sensors …

Properly leveraging resources within a continuum, and in particular the satisfaction of user
requirements, requires deep understanding of the workload itself and its interaction with the …

The deployment of workloads in a distributed environment is known to be complex tasks,
and it is even more complex when the system is expected to satisfy requirements in a …