QoS prediction for web service compositions using kernel-based quantile estimation with online adaptation of the constant offset

Services offered in a commercial context are expected to deliver certain levels of quality, typically contracted in a service level agreement (SLA) between the service provider and consumer. To prevent monetary penalties and loss of reputation by violating SLAs, it is important that the service provider can accurately estimate the Quality of Service (QoS) of all its provided (composite) services. This paper proposes a technique for predicting whether the execution of a service composition will be compliant with service level objectives (SLOs). We make three main contributions. First, we propose a simulation technique based on Petri nets to generate composite time series using monitored QoS data of its elementary services. This techniques preserves time related information and takes mutual dependencies between participating services into account. Second, we propose a kernel-based quantile estimator with online adaptation of the constant offset to predict future QoS values. The kernel-based quantile estimator is a powerful non-linear black-box regressor that (i) solves a convex optimization problem, (ii) is robust, and (iii) is consistent to the Bayes risk under rather weak assumptions. The online adaption guarantees that under certain assumptions the number of times the predicted value is worse than the actual value converges to the quantile value specified in the SLO. Third, we introduce two performance indicators for comparing different QoS prediction algorithms. Our validation in the context of two case studies shows that the proposed algorithms outperform existing approaches by drastically reducing the violation frequency of the SLA while maximizing the usage of the candidate services.