Web services run in complex contexts where arising events may compromise the quality of the whole system. Thus, it is desirable to count on autonomic mechanisms to guide the self-adaptation of service compositions according to changes in the computing infrastructure. One way to achieve this goal is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone. In this paper, we propose a solution based on a semantically rich variability model to support the dynamic adaptation of service compositions. When a problematic event arises in the context, this model is leveraged for decision-making. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. These changes are reflected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which can be deployed at runtime. In order to reach optimum adaptations, the variability model and its possible configurations are verified at design time using Constraint Programming. An evaluation demonstrates several benefits of our approach, both at design time and at runtime.

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Web service compositions run in complex computing infrastructures where arising events may affect the quality of the system. However, crucial Web service compositions cannot be stopped to apply changes to deal with problematic events. Therefore, the trend is moving towards context-aware Web service compositions, which use context information as a basis for autonomic changes. Under the closed-world assumption, the context and possible adaptations are fully known at design time. Nevertheless, it is difficult to foresee all the possible situations arising in uncertain contexts. In this article, we leverage models at runtime to guide the dynamic evolution of context-aware Web service compositions to deal with unexpected events in the open world. In order to manage uncertainty, a model that abstracts the Web service composition, self-evolves to preserve requirements. The evolved model guides changes in the underlying WS-BPEL composition schema. A software process model and its supporting method content are provided to guide the construction of models and other artifacts at design time. A prototype and an evaluation demonstrate the feasibility of our approach.

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Abstract. Model-driven techniques have proven to yield significant benefits for context-aware systems. Specifically, semantically-rich models are used at runtime to monitor the system context and guide necessary changes. Under the closedworld assumption, adaptations are fully known at design time. Nevertheless, it is difficult to foresee all the possible situations that may arise in uncertain and complex contexts. In this paper, we present a model-based framework to support the dynamic evolution of context-aware systems to deal with unexpected context events in the open world. If model adaptations are not enough to solve uncertainty, our model-based evolution planner guides the evolution of the supporting models to preserve high-level requirements. A case study about a context-aware Web service composition, which is executed in a distributed computing infrastructure, illustrates the applicability of our framework. A realization methodology and a prototype system support our approach.

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Several exceptional situations may arise in the complex, heterogeneous, and changing contexts where Web service operations run. For instance, a Web service operation may have greatly increased its execution time or may have become unavailable. The contribution of this article is to provide a tool-supported framework to guide autonomic adjustments of context-aware service compositions using models at runtime. At runtime, when problematic events arise in the context, models are used by an autonomic architecture to guide changes of the service composition. Under the closed-world assumption, the possible context events are fully known at design time. Nevertheless, it is difficult to foresee all the possible situations arising in uncertain contexts where service compositions run. Therefore, the proposed framework also covers the dynamic evolution of service compositions to deal with unexpected events in the open world. An evaluation demonstrates that our framework is efficient during dynamic adjustments.