Abstract
The number and complexity of embedded system platforms used in mixed-criticality applications are rapidly growing. They run large and evolving applications on heterogeneous multi- or manycore processing platforms requiring dependable operation and long lifetime. Examples include automated and autonomous driving, smart buildings, industry 4.0, and personal medical devices. The Information Processing Factory (IPF) applies principles inspired by factory management to master the complexity of future, highly- integrated embedded systems and to provide continuous operation and optimization at runtime. A general objective is to identify a sweet spot between a maximum of autonomy among IPF constituent components and a minimum of centralized control in order to ensure guaranteed service even under strict safety and availability requirements. This paper addresses the challenges of IPF and how to tackle them with a set of techniques: self-diagnosis for early detection of degradation and imminent failures combined with unsupervised platform self-adaptation to meet performance and safety targets.
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