Towards Model Checking Reconfigurable Petri Nets using Maude

This paper introduces an approach to model checking of reconfigurable Petri nets. The main task is to flatten the two levels of dynamic behavior that reconfigurable nets provide, the firing of transitions on the one hand and the transformation of the nets on the other hand. We show how to translate a reconfigurable net into  Maude modules. Maude's LTL model-checker is then used to verify properties of these modules

Hybrid performance modelling of opportunistic networks

We demonstrate the modelling of opportunistic networks using the process algebra stochastic HYPE. Network traffic is modelled as continuous flows, contact between nodes in the network is modelled stochastically, and instantaneous decisions are modelled as discrete events. Our model describes a network of stationary video sensors with a mobile ferry which collects data from the sensors and delivers it to the base station. We consider different mobility models and different buffer sizes for the ferries. This case study illustrates the flexibility and expressive power of stochastic HYPE. We also discuss the software that enables us to describe stochastic HYPE models and simulate them.Comment: In Proceedings QAPL 2012, arXiv:1207.055

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

A Conceptual Framework for Adapation

We present a white-box conceptual framework for adaptation. We called it CODA, for COntrol Data Adaptation, since it is based on the notion of control data. CODA promotes a neat separation between application and adaptation logic through a clear identification of the set of data that is relevant for the latter. The framework provides an original perspective from which we survey a representative set of approaches to adaptation ranging from programming languages and paradigms, to computational models and architectural solutions

Leveraging service-oriented business applications to a rigorous rule-centric dynamic behavioural architecture.

Today’s market competitiveness and globalisation are putting pressure on organisations to join their efforts, to focus more on cooperation and interaction and to add value to their businesses. That is, most information systems supporting these cross-organisations are characterised as service-oriented business applications, where all the emphasis is put on inter-service interactions rather than intra-service computations. Unfortunately for the development of such inter-organisational service-oriented business systems, current service technology proposes only ad-hoc, manual and static standard web-service languages such as WSDL, BPEL and WS-CDL [3, 7]. The main objective of the work reported in this thesis is thus to leverage the development of service-oriented business applications towards more reliability and dynamic adaptability, placing emphasis on the use of business rules to govern activities, while composing services. The best available software-engineering techniques for adaptability, mainly aspect-oriented mechanisms, are also to be integrated with advanced formal techniques. More specifically, the proposed approach consists of the following incremental steps. First, it models any business activity behaviour governing any service-oriented business process as Event-Condition-Action (ECA) rules. Then such informal rules are made more interaction-centric, using adapted architectural connectors. Third, still at the conceptual-level, with the aim of adapting such ECA-driven connectors, this approach borrows aspect-oriented ideas and mechanisms, and proposes to intercept events, select the properties required for interacting entities, explicitly and separately execute such ECA-driven behavioural interactions and finally dynamically weave the results into the entities involved. To ensure compliance and to preserve the implementation of this architectural conceptualisation, the work adopts the Maude language as an executable operational formalisation. For that purpose, Maude is first endowed with the notions of components and interfaces. Further, the concept of ECA-driven behavioural interactions are specified and implemented as aspects. Finally, capitalising on Maude reflection, the thesis demonstrates how to weave such interaction executions into associated services

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA

RDF : un modèle flot de données reconfigurable(version étendue)

Dataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF (and most of its variants) lacks the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment.We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and outline its implementation.Les modèles de calcul (MoCs) flot de données synchrones sont très utilisés dans les systèmes embarqués pour les applications multimédia, de traitement du signal, de télécommunication et de contrôle automatique. Dans ce style de modèle, une application est spécifiée par un graphe d’acteurs connectés par des liens FIFO de communication. Un des MoCs les plus connus, SDF (pour Synchronous Dataflow), permet des analyses statiques qui garantissent l’exécution enmémoire bornée et l’absence d’interblocage, propriétés clés pour les systèmes embarqués. Néanmoins, SDF (et la plupart de ses variantes) ne permet pas d’exprimer la dynamicité requise par les applications embarquées modernes. En particulier, ces applications ont souvent besoin de se reconfigurer pour s’adapter aux changements (par ex., de débit ou de qualité) du flot d’entrée, des objectifs de contrôle ou de l’environnement.Afin de répondre à ce besoin, nous proposons le MoC RDF (pour Reconfigurable DataFlow) qui étend SDF avec des règles de transformations spécifiant comment la topologie et les acteurs du graphe peuvent être reconfigurés dynamiquement. En considérant un graphe SDF initial et un ensemble de règles de transformation, un nombre arbitraire de nouveaux graphes peuvent être produits. La principale qualité de RDF est qu’il peut être analysé statiquement pour garantir que tous les graphes générés dynamiquement s’exécuteront en mémoire bornée et sans interblocage.Nous présentons le modèle RDF, décrivons les analyses statiques associées et décrivons brièvementson implémentation