Abstract. In modern pervasive dynamic and eternal systems, software must be able to self-organize its structure and self-adapt its behavior to enhance its resilience and provide the desired quality of service. In this high-dynamic and unpredictable scenario, flexible and reconfigurable monitoring infrastructures become key instruments to verify at runtime functional and non-functional properties. In this paper, we propose a property-driven approach to runtime monitoring that is based on a comprehensive Property Meta-Model (PMM) and on a generic configurable monitoring infrastructure. PMM supports the definition of quantitative and qualitative properties in a machine-processable way making it possible to configure the monitors dynamically. Examples of implementation and applications of the proposed model-driven monitoring infrastructure are excerpted from the ongoing Connect European Project.
In service-oriented systems non-functional properties become very important to support run-time service discovery and composition. Software engineers should take care of them for guaranteeing the service quality in all the software life-cycle phases, from requirements specification to design, to system deployment and execution monitoring. This wide scope and the criticality of non-functional properties demand that they are expressed in a language which is intuitive and easy to use for the service quality specification, and at the same time is machine-processable to be automatically handled at run-time. In this paper we present a Property Meta-Model that aims to reach these two main objectives and show as a proof of concept its use for the modeling of two different properties.
Nowadays, more and more industrial organizations are using Business Process Model and Notation (BPMN) for process modeling. Key performance Indicators (KPIs) are set on such process models so to get a quantitative assessment of critical success metrics. A timely and reliable monitoring of KPIs is instrumental to Business Process (BP) management, and several frameworks are being proposed for such purpose. Business process monitoring solutions can be embedded into the Business Process Modeling (BPM) execution framework or integrated as additional facilities. This paper presents an integrated framework that allows for modeling, execution and analysis of business process based on a flexible and adaptable monitoring infrastructure. The main advantage of the proposed approach is that it is independent from any specific business process modeling notation and execution engine and allows for the definition and evaluation of user-specific KPI measures.
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