End-to-End QoS Prediction of Vertical Service Composition in the Cloud

In a cloud-based service selection system, for a given request, there could be a large number of software services matching the functional requirements. The selection should then be done based on their QoS values. Since in a cloud environment, a software service might need collaboration from other types of cloud services (e.g., a software service delivered through an infrastructure service) to offer a complete solution to an end user, the selection system should have a way to measure the QoS values of the whole solution, instead of QoS of software services alone. This kind of end-to-end QoS values of cloud-based software solutions may or may not be available in recorded history logs. In this paper, we propose a model for predicting end-to-end QoS values of cloud-based software solutions composed of services from multiple cloud layers. It relies on the internal features of services and end users such as locations, configurations, functionality, and user profiles to calculate service similarity and then predict QoS values. The experiments demonstrate the accuracy of our approach. We also studied the impact of the proposed internal features on QoS prediction accuracy.