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Erschienen in:
01.03.2019 | Original Research Paper
An adaptive and scalable framework for automated service discovery
Erschienen in: Service Oriented Computing and Applications | Ausgabe 1/2019
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Abstract
A large number of redundant Web services, offering similar functionality with different quality of service (QoS), can truly benefit an organization, if offered an adaptive discovery framework. State-of-the-art approaches cannot meet these requirements for enterprise setups that offer a managed infrastructure. This paper proposes a self-healing and self-managed open-source adaptive QoS-aware discovery framework. An optimal global optimization approach is developed as part of the framework. It yields the shortest route to the best service. Simulation results demonstrate the suitability of the proposed framework to run-time QoS changes and service failures. The run-time adaptation of the system during service downtime is analyzed using graphs. These graphs illustrate the change in client QoS constraints against time. The results indicate the least impact on the client during service failure using the proposed framework.