Towards Data-Aware QoS-driven Adaptation for Service Orchestrations

Several activities in service oriented computing can benefit from knowing properties of a given service composition ahead of time. We will focus here on properties related to computational cost and resource usage, in a wide sense, as they can be linked to QoS characteristics. In order to attain more accuracy, we formulate computational cost / resource usage as functions on input data (or appropriate abstractions thereof) and show how these functions can be used to make more informed decisions when performing composition, proactive adaptation, and predictive monitoring. We present an approach to, on one hand, automatically synthesize these functions from orchestrations and, on the other hand, to effectively use them to increase the quality of non-trivial service-based systems with data-dependent behavior. We validate our approach by means of simulations with runtime selection of services and adaptation due to service failure.