Towards accurate failure prediction for the proactive adaptation of service-oriented systems
Proceedings of the 8th workshop on Assurances for self-adaptive systems
Future internet apps: the next wave of adaptive service-oriented systems?
ServiceWave'11 Proceedings of the 4th European conference on Towards a service-based internet
A constraint-based approach to quality assurance in service choreographies
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
Adequate monitoring of service compositions
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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Increasingly, service-based applications (SBAs) are composed of third-party services available over the Internet. Even if third-party services have shown to work during design-time, they might fail during the operation of the SBA due to changes in their implementation, provisioning, or the communication infrastructure. As a consequence, SBAs need to dynamically adapt to such failures during run-time to ensure that they maintain their expected functionality and quality. Ideally the need for an adaptation is proactively identified, i.e., failures are predicted before they can lead to consequences such as costly compensation and roll-back activities. Currently, approaches to predict failures are based on monitoring. Due to its passive nature, however, monitoring might not cover all relevant service executions, which can diminish the ability to correctly predict failures. In this paper we demonstrate how online testing, as an active approach, can improve failure prediction by considering a broader range of service executions. Specifically, we introduce a framework and prototypical implementation that exploits synergies between monitoring, online testing and quality prediction. For online test selection and assessment we adapt usage-based testing strategies. We experimentally evaluate the strengths of our approach in predicting the need for an adaptation of an SBA.