Gaussian process regression as a predictive model for Quality-of-Service in Web service systems
This addresses performance prediction for Web service systems, but appears incremental as it applies an existing method to a specific domain.
The paper tackled predicting Quality-of-Service attributes in Web service systems using Gaussian process regression, and found it performed best with a linear kernel, showing statistically significant improvement over Classification and Regression Trees in terms of Mean Absolute Error and Mean Squared Error.
In this paper, we present the Gaussian process regression as the predictive model for Quality-of-Service (QoS) attributes in Web service systems. The goal is to predict performance of the execution system expressed as QoS attributes given existing execution system, service repository, and inputs, e.g., streams of requests. In order to evaluate the performance of Gaussian process regression the simulation environment was developed. Two quality indexes were used, namely, Mean Absolute Error and Mean Squared Error. The results obtained within the experiment show that the Gaussian process performed the best with linear kernel and statistically significantly better comparing to Classification and Regression Trees (CART) method.