Summary Service-Oriented Computing (SOC) enables the composition of loosely coupled service agents provided with varying Quality of Service (QoS) levels, effectively forming a multiagent system (MAS). Selecting a (near-)optimal set of services for a composition in terms of QoS is crucial when many functionally equivalent services are available. As the number of distributed services, especially in the cloud, is rising rapidly, the impact of the network on the QoS keeps increasing. Despite this and opposed to most MAS approaches, current service approaches depend on a centralized architecture which cannot adapt to the network. Thus, we propose a scalable distributed architecture composed of a flexible number of distributed control nodes. Our architecture requires no changes to existing services and adapts from a centralized to a completely distributed realization by adding control nodes as needed. Also, we propose an extended QoS aggregation algorithm that allows to accurately estimate network QoS. Finally, we evaluate the benefits and optimality of our architecture in a distributed environment.
Service selection is a central challenge in the context of a Service Oriented Architecture. Once functionally sufficient services have been selected, a further selection based on non-functional properties (NFPs) becomes essential in meeting the user's requirements and preferences. However, current descriptions of NFPs and approaches to NFP-aware selection lack the ability to handle the variability of NFPs, that stems from the complex nature of real-world business scenarios. Therefore, we propose a probabilistic approach to service selection as follows: First, to address the inherent variability in the actual values of NFPs at runtime, we treat them as probability distributions. Then, on top of that, we tackle the variability needed in describing NFPs, by providing conditional contracts. Finally, from usage patterns, we compute user-specific expectations for such NFPs. Further, we depict a typical scenario, which serves both as a motivation for our approach, and as a basis for its evaluation. Quality of Services (QoS) plays an essential role in realizing user tasks by service composition. Most QoS-aware service composition approaches have ignored the fact that QoS values can depend on the time of execution. Common QoS attributes such as response time may depend for instance on daytime, due to access tendency or conditional Service of Level Agreements. Application-specific QoS attributes often have tight relationships with the current state of resources, such as availability of hotel rooms. In response to these problems, this paper proposes an integrated multi-objective approach to QoS-aware service composition and selection.