Automated statistical forecasting for quality attributes of web services

Web services provide a standardized solution for service-oriented architecture. They are characterized by quality of service (QoS) attributes monitored to ensure conformance to requirements. However, the reactive detection of QoS violations can lead to critical problems as the violation has already occurred and consequent costs may be unavoidable. To address these problems, this thesis proposes a collection of QoS characteristic-specific automated forecasting approaches based on time-series modeling. These forecasting approaches provide the basis for a general automated forecasting approach for QoS attributes. The accuracy and performance of the proposed forecasting approaches are evaluated using real-world QoS datasets of Web services.