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SUMMARYA strong separation of concerns is necessary in order to make the design of domain-specific functional components independent from cross-cutting concerns, such as concurrent access to the shared resources of the execution platform. Native coordination mechanisms, such as locks and monitors, allow developers to address these issues. However, such solutions are not modular, they are complex to design, debug and maintain. We present the JavaBIP framework that allows developers to think on a higher level of abstraction and clearly separate the functional and coordination aspects of the system behavior. It implements the principles of the BIP component framework rooted in rigorous operational semantics. It allows the coordination of existing concurrent software components in an exogenous manner, relying exclusively on annotations, component APIs and external specification files. We introduce the annotation and specification syntax of JavaBIP and illustrate its use on realistic examples; present the architecture of our implementation, which is modular and easily extensible; provide and discuss performance evaluation results.

Multi-valued decision diagrams (MDDs) are a convenient approach to representing many kinds of constraints including table constraints, regular constraints, complex set and multiset constraints, as well as ad-hoc problem specific constraints. This paper introduces an incremental propagation algorithm for MDDs, and explores several methods for incorporating explanations with MDD-based propagators. We demonstrate that these techniques can provide significantly improved performance when solving a variety of problems.

Coordinating component behaviour and access to resources is among the key difficulties of building large concurrent systems. To address this, developers must be able to manipulate high-level concepts, such as Finite State Machines and separate functional and coordination aspects of the system behaviour. OSGi associates to each bundle a state machine representing the bundle's lifecycle. However, once the bundle has been started, it remains in the state Active-the functional states are not represented. Therefore, this mechanism is not sufficient for coordination of active components.In this paper, we present a methodology for functional component coordination in OSGi by using BIP coordination mechanisms. BIP allows us to clearly separate the systemwide coordination policies from the component behaviour and the interface that components expose for interaction. By using BIP, we show how the allowed global states and state transitions of the modular system can be taken into account in a non-invasive manner and without any impact on the technology stack within an OSGi container.

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This work concentrates on improving the robustness of constraint solvers by increasing the propagation strength of constraint models in a declarative and automatic manner. Our objective is to efficiently identify and remove shavable values during search. A value is shavable if as soon as it is assigned to its associated variable an inconsistency can be detected, making it possible to refute it. We extend previous work on shaving by using different techniques to decide if a given value is an interesting candidate for the shaving process. More precisely, we exploit the semantics of (global) constraints to suggest values, and reuse both the successes and failures of shaving later in search to tune shaving further. We illustrate our approach with two important global constraints, namely alldifferent and sum, and present the results of an experimentation obtained for three problem classes. The experimental results are quite encouraging: we are able to significantly reduce the number of search nodes (even by more than two orders of magnitude), and improve the average execution time by one order of magnitude.

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This paper presents a novel partial assignment technique (PAT) that decides which tasks should be assigned to the same resource without explicitly defining assignment of these tasks to a particular resource. Our method simplifies the assignment and scheduling steps while imposing a small or no penalty on the final solution quality. This technique is specially suited for problems which have different resources constraints. Our method does not cluster tasks into a new task, as typical clustering techniques do, but specifies which tasks need to be executed on the same processor. Our experiments have shown that PAT, which may produce nonlinear groups of tasks, gives better results than linear clustering when multi-resource constraints are present. Linear clustering was proved to be optimal comparing to all other clusterings for problems with timing constraints only. In this paper, we show that, if used for multi-resource synthesis problem, as it is often used nowadays, linear clustering will produce inferior solutions.