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Abstract-A simple and effective composition of software services into higher-level composite services is still a very challenging task. Especially in enterprise environments, Quality of Service (QoS) concerns play a major role when building software systems following the Service-Oriented Architecture (SOA) paradigm. In this paper we present a composition approach based on a domainspecific language (DSL) for specifying functional requirements of services and the expected QoS in form of constraint hierarchies by leveraging hard and soft constraints. A composition runtime will resolve the user's constraints to find an optimized composition semi-automatically. To this end we leverage data flow analysis to generate a structured composition model and use two different techniques for the optimization, a constraint programming and an integer programming approach.

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Abstract. SLAs are contractually binding agreements between service providers and consumers, mandating concrete numerical target values which the service needs to achieve. For service providers, it is essential to prevent SLA violations as much as possible to enhance customer satisfaction and avoid penalty payments. Therefore, it is desirable for providers to predict possible violations before they happen, while it is still possible to set counteractive measures. We propose an approach for predicting SLA violations at runtime, which uses measured and estimated facts (instance data of the composition or QoS of used services) as input for a prediction model. The prediction model is based on machine learning regression techniques, and trained using historical process instances. We present the basics of our approach, and briefly validate our ideas based on an illustrative example.

In service-oriented systems, Quality of Service represents an important issue which is often considered when selecting and composing services. For receiving up-to-date information, non-functional properties such as response time or availability can be continuously monitored using server-or clientside approaches. However, both approaches have strengths and weaknesses. In this paper, we present a framework that combines the advantages of client-and server-side QoS monitoring. It builds on event processing to inform interested subscribers of current QoS values and possible violations of Service Level Agreements. These events can trigger adaptive behavior such as hosting new service instances if the QoS is not as desired. We describe our QoS monitoring approach in detail, show how it was integrated into the VRESCo service runtime environment, and evaluate the accuracy of the presented monitoring techniques.