AI4AS

Modern computing systems are large and heterogeneous. Their complexity is hardly manageable by a human being, especially when it comes to taking timely decisions in highly dynamic environments or to guarantee strict Quality-of-Service requirements. Not surprisingly, recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) significantly impacted and fostered the development of autonomous computing systems, providing new or enhanced methodologies to cope with system complexity and uncertainty. AI and ML techniques are increasingly adopted to assist or guide system self-adaptation, as they are used, e.g., to extract relevant information from highly dimensional and noisy monitoring data, to predict internal or external dynamics, to automatically plan adaptation actions... However, there are still several challenges to face for researchers and practitioners aiming to take advantage of these methodologies and incorporate them in their systems. Fundamental issues towards..