Analysis, Design, and Control of Predictable Interconnected Systems

We call “Interconnected Systems” any collection of systems distributed over a metric space whose behavior is influenced by its neighborhood. Examples of interconnected systems exist at very different scales: different cores over the same silicon, different sub-systems in vehicles, communicating nodes over either a physical (e.g., optical) network, or — more recently — virtualized network. Examples also exist in contexts which are not related to computing or communication. Smart Grids (of energy production, distribution, and consumption) and Intelligent Transportation Systems are just two notable examples. The common characteristic among all these examples is the presence of a spatially distributed demand of resources (energy, computing, communication bandwidth, etc.) which needs to be matched with a spatially distributed supply. Often times demands and availability of resources of different types (e.g., computing and link bandwidth in virtualized network environments) need to be matched simultaneously. Time predictability is a key requirement for above systems. Despite this, the strong market pressure has often led to “quick and dirty” best-effort solutions, which make it extremely challenging to predict the behavior of such systems. Research communities have developed formal theories for predictability which are specialized to each application domain or type of resource (e.g., schedulability analysis for real-time systems or network calculus for communication systems). However, the emerging application domains (virtualized networks, cyber-physical systems, etc.) clearly require a unified, holistic approach. By leveraging the expertise, vision and interactions of scientists that have addressed predictability in different areas, the proposed seminar aims at constructing a common ground for the theory supporting the analysis, the design, and the control of predictable interconnected systems