PIACERE: Programming trustworthy Infrastructure As Code in a Secure Framework

Infrastructure-as-Code (IaC), enables the automation of several deployment, configuration and management tasks. IaC has a lot of potential in cloud computing as it results in a significant saving of time when an application needs to be redeployed on a different set of resources, even running on different infrastructures. Unfortunately, IaC still suffers from some important issues, such as the large variety of competing tools or the strong orientation toward the cloud, leaving aside e.g. the edge. Also, trustworthiness and security aspects of are often left for the end of the cycle, where errors and vulnerabilities are often too late or too expensive to correct. We present here the PIACERE project, which provides tools, methods and techniques for the Infrastructure-as-Code approach. The project will make the creation of IaC more accessible to designers, developers and operators, increasing the quality, security, trustworthiness and evolvability of infrastructural code while ensuring its business continuity by providing self-healing mechanisms anticipation of failures and violations.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101000162

Qualitatively faithful quantitative prediction

We describe an approach to machine learning from numerical data that combines both qualitative and numerical learning. This approach is carried out in two stages: (1) induction of a qualitative model from numerical examples of the behaviour of a physical system, and (2) induction of a numerical regression function that both respects the qualitative constraints and fits the training data numerically. We call this approach Q2 learning, which stands for Qualitatively faithful Quantitative learning. Induced numerical models are “qualitatively faithful” in the sense that they respect qualitative trends in the learning data. Advantages of Q2 learning are that the induced qualitative model enables a (possibly causal) explanation of relations among the variables in the modelled system, and that numerical predictions are guaranteed to be qualitatively consistent with the qualitative model which alleviates the interpretation of the predictions. Moreover, as we show experimentally the qualitative model’s guidance of the quantitative modelling process leads to predictions that may be considerably more accurate than those obtained by state-of-the-art numerical learning methods. The experiments include an application of Q2 learning to the identification of a car wheel suspension system—a complex, industrially relevant mechanical system