The [email protected] paradigm promotes the use of models during the execution of cyber-physical systems to represent their context and to reason about their runtime behaviour. However, current modeling techniques do not allow to cope at the same time with the large-scale, distributed, and constantly changing nature of these systems. In this paper, we introduce a distributed [email protected] approach, combining ideas from reactive programming, peer-to-peer distribution, and large-scale [email protected]. We define distributed models as observable streams of chunks that are exchanged between nodes in a peer-to-peer manner. A lazy loading strategy allows to transparently access the complete virtual model from every node, although chunks are actually distributed across nodes. Observers and automatic reloading of chunks enable a reactive programming style. We integrated our approach into the Kevoree Modeling Framework and demonstrate that it enables frequently changing, reactive distributed models that can scale to millions of elements and several thousand nodes.

Internet of Things applications analyze our past habits through sensor measures to anticipate future trends. To yield accurate predictions, intelligent systems not only rely on single numerical values, but also on structured models aggregated from different sensors. Computation theory, based on the discretization of observable data into timed events, can easily lead to millions of values. Time series and similar database structures can efficiently index the mere data, but quickly reach computation and storage limits when it comes to structuring and processing IoT data. We propose a concept of continuous models that can handle high-volatile IoT data by defining a new type of meta attribute, which represents the continuous nature of IoT data. On top of traditional discrete object-oriented modeling APIs, we enable models to represent very large sequences of sensor values by using mathematical polynomials. We show on various IoT datasets that this significantly improves storage and reasoning efficiency.

International audience

Since software systems need to be continuously available under varying conditions, their ability to evolve at runtime is increasingly seen as one key issue. Modern programming frameworks already provide support for dynamic adaptations. However the high-variability of features in Dynamic Adaptive Systems (DAS) introduces an explosion of possible runtime system configurations (often called modes) and mode transitions. Designing these configurations and their transitions is tedious and error-prone, making the system feature evolution difficult. While Aspect-Oriented Modeling (AOM) was introduced to improve the modularity of software, this paper presents how an AOM approach can be used to tame the combinatorial explosion of DAS modes. Using AOM techniques, we derive a wide range of modes by weaving aspects into an explicit model reflecting the runtime system. We use these generated modes to automatically adapt the system. We validate our approach on an adaptive middleware for home-automation currently deployed in Rennes metropolis.