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Quality of Services has an essential role throughout the process of Cloud-based web service composition for faultless and dynamic integration of business applications. Nonetheless, as Cloud-based web services grow, it becomes complicated to make possible service composition rapidly. Because web services response time can be a priceless tool for CWS (Cloud-based web service) composition satisfying end users QoS requirements, in this study we search for the best adaptive model that can do this task.

Regardless of new achievements in the research of prediction models, QoS is still a great issue for high quality web services and remains one of the key subjects that need to be studied. We believe that QoS should not only be measured, but have to be predicted in development and implementation phases. In this paper we assess how different input projection algorithms influence the prediction accuracy of a Multi-Layer Perceptron (MLP) trained with large datasets of web services QoS values.

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