The “Compute Continuum” paradigm is transforming how we manage the heterogeneity and dynamism of widespread computing resources. By seamlessly integrating resources across the edge, fog, and cloud — and potentially extending towards emerging domains such as satellite-enabled infrastructures — this paradigm enhances data locality, performance, availability, adaptability, energy and carbon efficiency, and other non-functional properties. This is made possible by overcoming resource fragmentation and segregation in tiers, allowing applications to be seamlessly executed and relocated along a continuum of resources spanning from the edge to the cloud. Besides consolidated vertical and horizontal scaling patterns, the Compute Continuum also introduces fine-grained adaptation actions tailored to specific infrastructure components (e.g., optimizing energy consumption, offloading computation across heterogeneous links, or leveraging specialized hardware such as GPUs and FPGAs). These capabilities unlock significant benefits, including support for latency-sensitive applications, reduction of network bandwidth consumption, improved privacy protection, and the development of novel services across domains such as smart cities, healthcare, safety, and mobility. All of this should be achievable by application developers without having to worry about how and where the developed components will be executed.

At the same time, traditional deployment and execution assumptions are increasingly challenged by the dynamism and heterogeneity of continuum infrastructures. Lightweight execution environments and runtimes — including unikernels, microVMs, and WebAssembly-based sandboxes — are emerging as promising solutions to reduce overheads and enable fine-grained control over resource usage, while raising new challenges for orchestration, coordination, and scheduling across the continuum.

To fully harness the potential of the Compute Continuum, proactive, autonomous, and infrastructure-aware management is essential. This calls for novel interdisciplinary approaches that exploit optimization theory, control theory, machine learning, and artificial intelligence methods.

Important dates

Submission deadline: May 15, 2026
Notification of acceptance: June 12, 2026
Camera-ready deadline: July 10, 2026
Workshop dates: August 24 or 25, 2026