Abstract-Business activity monitoring enables continuous observation of key performance indicators (KPIs). However, if things go wrong, a deeper analysis of process performance becomes necessary. Business analysts want to learn about the factors that influence the performance of business processes and most often contribute to the violation of KPI target values, and how they relate to each other. We provide a framework for performance monitoring and analysis of WS-BPEL processes, which consolidates process events and Quality of Service measurements. The framework uses machine learning techniques in order to construct tree structures, which represent the dependencies of a KPI on process and QoS metrics. These dependency trees allow business analysts to analyze how the process KPIs depend on lower-level process metrics and QoS characterisitics of the IT infrastructure. Deeper knowledge about the structure of dependencies can be gained by drill-down analysis of single factors of influence.
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.
Business process monitoring in the area of service oriented computing is typically performed using business activity monitoring technology in an intra-organizational setting. Due to outsourcing and the increasing need for companies to work together to meet their joint customer demands, there is a need for monitoring of business processes across organizational boundaries. Thereby, partners in a choreography have to exchange monitoring data, in order to enable process tracking and evaluation of process metrics. In this paper, we describe an event-based monitoring approach based on BPEL4Chor service choreography descriptions. We show how to define monitoring agreements specifying events each partner in the choreography has to provide. We distinguish between resource events and complex events for calculation of process metrics using complex event processing technology. We present our implementation and evaluate the concepts based on a scenario.
Abstract. Service-based applications have become more and more multilayered in nature, as we tend to build software as a service on top of infrastructure as a service. Most existing SOA monitoring and adaptation techniques address layer-specific issues. These techniques, if used in isolation, cannot deal with real-world domains, where changes in one layer often affect other layers, and information from multiple layers is essential in truly understanding problems and in developing comprehensive solutions. In this paper we propose a framework that integrates layer specific monitoring and adaptation techniques, and enables multi-layered control loops in service-based systems. The proposed approach is evaluated on a medical imaging procedure for Computed Tomography (CT) Scans, an eHealth scenario characterized by strong dependencies between the software layer and infrastructural resources.
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