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Monitoring and analysis of QoS are crucial steps for the provisioning of self-healing web services and for managing web service-based distributed interactive applications. Dealing with these issues becomes even more challenging when applications are dynamically built by composition of distributed services involving different service providers. In this case, assuming access to the internal logic and its implementation within the composed web services is not realistic. In this paper, we propose an architectural framework for monitoring and analysis of QoS driven by models for QoS analysis. This framework has been implemented and experimented for the web service technology within the European WS-DIAMOND 1 project. We consider the general context where only SOAP messages between web services are monitored. The main novelty of our approach is, on the one hand, to provide a generic application-independent framework. On the other hand, we provide models allowing QoS deficiencies to be detected and considered as an indicator of the health degradation of the monitored web services.

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Architectural adaptation is important for handling self-configuring properties of autonomic distributed systems. It can be achieved by model-based management of dynamic architectures. Describing dynamic architectures includes defining rules for reconfiguration management. Within this research context, several works have been conducted using formal specification to handle this complexity. Graph and graph rewriting-based approaches showed, through many studies, their appropriateness to tackle architectural adaptation problems. However, scalability of such approaches remains an open issue and has been rarely explored. In this paper, we investigate this issue. We introduce a graph-based general approach for handling of dynamic architectures, and we illustrate it within a scenario of collaboration support in Crisis Management Systems. We elaborate the formal models for dynamic architecture management. Using the French Grid GRID5000, we conducted an experimental study to assess the scalability of the elaborated models.

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