Geographic Location-Based Network-Aware QoS Prediction for Service Composition

QoS-aware service composition intends to maximize the global QoS of a composite service while selecting candidate services from different providers with local and global QoS constraints. With more and more candidate services emerging from all over the world, the network delays often greatly impact the performance of the composite service, which are usually not easy to be collected before the composition. One remedy is to predict them for the composition. However, new issues occur in predicting network delay for the composition, including prediction accuracy and on-demand measures to new services, which affect the performance of network-aware composite services. To solve these critical challenges, in this paper, we take advantage of the geographic location information of candidate services. We propose a network-aware QoS (NQoS) model for the composite service. Based on that, we present a novel geographic location-based NQoS prediction approach before composition, and a NQoS re-prediction approach during the execution of the composite service. Extensive experiments are conducted on the real-world dataset collected from PlanetLab. Comparative experiment results reveal our approach facilitates to improve the prediction accuracy and predictability of the NQoS values, and increase global NQoS of the composite service while ensuring its reliability constraints.