A novel scalable representative-based forecasting approach of service quality

Several approaches to forecast the service quality based on its quality of service (QoS) properties are reported in the literature. However, their main disadvantage resides in their limited scalability. In fact, they elaborate a forecasting model for each quality attribute per service, which cannot scale well for large or even medium size datasets of services. Accordingly, we propose a novel scalable representative-based forecasting approach of QoS. The QoS is modeled as a multivariate time series in which the values of service attributes are evaluated at each time instant and forecasted based on three stages. First, a data aggregation function is applied to the multivariate time series data. Then, principal component analysis (PCA) is applied to the quality attributes to determine the most relevant ones. The reduced data is then clustered, so that, a representative for each cluster is computed. Finally, a forecasting model is built for each cluster representative for the sake of deriving other services’ forecasting models. A set of extensive experiments are carried out to assess the efficiency and accuracy of the proposed approach on a dataset of real services. The experimental results show that the proposed approach is up to 75% more efficient than direct forecasting approaches using time measurements while increasing the number of forecasted services and that the elaborated forecasting models enjoy insignificant forecasting errors.