178 citations · 207 across the 7 of their papers we have counts for
8 papers
Service Orchestration in the Computing Continuum: Structural Challenges and Vision
Boris Sedlak, Víctor Casamayor Pujol, Ildefons Magrans de Abril +3
The Computing Continuum (CC) integrates different layers of processing infrastructure, from Edge to Cloud, to optimize service quality through ubiquitous and reliable computation.…
Multi-dimensional Autoscaling of Processing Services: A Comparison of Agent-based Methods
Boris Sedlak, Alireza Furutanpey, Zihang Wang +2
Edge computing breaks with traditional autoscaling due to strict resource constraints, thus, motivating more flexible scaling behaviors using multiple elasticity dimensions. This w…
Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning
Anastasiya Danilenka, Alireza Furutanpey, Victor Casamayor Pujol +5
Handling heterogeneity and unpredictability are two core problems in pervasive computing. The challenge is to seamlessly integrate devices with varying computational resources in a…
Modern Computing: Vision and Challenges
Sukhpal Singh Gill, Huaming Wu, Panos Patros +22
Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Int…
Distributed AI in Zero-touch Provisioning for Edge Networks: Challenges and Research Directions
Abhishek Hazra, Andrea Morichetta, Ilir Murturi +5
Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively…
Learning-driven Zero Trust in Distributed Computing Continuum Systems
Ilir Murturi, Praveen Kumar Donta, Victor Casamayor Pujol +2
Converging Zero Trust (ZT) with learning techniques can solve various operational and security challenges in Distributed Computing Continuum Systems (DCCS). Implementing centralize…
Active Inference on the Edge: A Design Study
Boris Sedlak, Victor Casamayor Pujol, Praveen Kumar Donta +1
Machine Learning (ML) is a common tool to interpret and predict the behavior of distributed computing systems, e.g., to optimize the task distribution between devices. As more and…
Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration
Henna Kokkonen, Lauri Lovén, Naser Hossein Motlagh +15
Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration…