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 homeautomation currently deployed in Rennes metropolis.

Considered is a mobile ad hoc network consisting of three types of nodes (source, destination and relay nodes) and using the two-hop relay routing protocol. Packets at relay nodes are assumed to have a limited lifetime in the network. All nodes are moving inside a bounded region according to some random mobility model. Both closed-form expressions, and asymptotic results when the number of nodes is large, are provided for the packet delivery delay and the energy needed to transmit a packet from the source to its destination. We also introduce and evaluate a variant of the two-hop relay protocol that limits the number of generated copies in the network. Our model is validated through simulations for two mobility models (random waypoint and random direction mobility models), numerical results for the two-hop relay protocols are reported, and the performance of the two-hop routing and of the epidemic routing protocols are compared.

Abstract. Models@Runtime aims at taming the complexity of software dynamic adaptation by pushing further the idea of reflection and considering the reflection layer as a first-class modeling space. A natural approach to Models@Runtime is to use MDE techniques, in particular those based on the Eclipse Modeling Framework. EMF provides facilities for building DSLs and tools based on a structured data model, with tight integration with the Eclipse IDE. EMF has rapidly become the defacto standard in the MDE community and has also been adopted for building Models@Runtime platforms. For example, Frascati (implementing the Service Component Architecture standard) uses EMF for the design and runtime tooling of its architecture description language. However, EMF has primarily been thought to support design-time activities. This paper highlights specific Models@Runtime requirements, discusses the benefits and limitations of EMF in this context, and presents an alternative implementation to meet these requirements.

User interface adaptations can be performed at runtime to dynamically reflect any change of context. Complex user interfaces and contexts can lead to the combinatorial explosion of the number of possible adaptations. Thus, dynamic adaptations come across the issue of adapting user interfaces in a reasonable time-slot with limited resources. In this paper, we propose to combine aspect-oriented modeling with property-based reasoning to tame complex and dynamic user interfaces. At runtime and in a limited time-slot, this combination enables efficient reasoning on the current context and on the available user interface components to provide a well suited adaptation. The proposed approach has been evaluated through EnTiMid, a middleware for home automation.

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Abstract. Models@run.time provides semantically rich reflection layers enabling intelligent systems to reason about themselves and their surrounding context. Most reasoning processes require not only to explore the current state, but also the past history to take sustainable decisions e.g. to avoid oscillating between states. Models@run.time and model-driven engineering in general lack native mechanisms to efficiently support the notion of history, and current approaches usually generate redundant data when versioning models, which reasoners need to navigate. Because of this limitation, models fail in providing suitable and sustainable abstractions to deal with domains relying on history-aware reasoning. This paper tackles this issue by considering history as a native concept for modeling foundations. Integrated, in conjunction with lazy load/storage techniques, into the Kevoree Modeling Framework, we demonstrate onto a smart grid case study, that this mechanisms enable a sustainable reasoning about massive historized models.

Abstract-Ageing populations and the necessity to reduce environmental impact raise new challenges on our living buildings. Convergence of home control systems (air conditioning, light management) and computer science, or house automation, allows to enhance comfort, security and health of inhabitants, and reduce energy consumption. Each of these abilities can be perceived as a service provided by the house automation system. Starting from this point, we developed ENTIMID , a middleware able to make systems from different brands cooperate in a single service-based platform. Yet the proliferation and variability of such services, and needs to tailor each system to a particular building, make the design of these systems complex. In this prospective paper, we explain how the notion of dynamic software product line facilitates such designs by providing sophisticated techniques for managing variability across services from design time to runtime and allowing their automatic composition.

Abstract-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.