Distributed applications are rapidly converging towards the adoption of a computing paradigm based on service-oriented architectures (SOA), according to which an application results from the composition of a set of services in execution on networked server hosts. In a SOA context, service providers are strategically interested both to describe the performance characteristics of offered services, to better qualify their offer and gain a significant advantage in the global marketplace, and to predict the level of performance that can be offered to service consumers when building composite web services that make use of services managed by various service providers. This paper introduces a model-driven approach for integrating performance prediction into service composition processes carried out by use of BPEL (Business Process Execution Language for Web Services). The proposed approach is founded on P-WSDL (Performance-enabled WSDL), a performance-oriented extension of WSDL, the language for describing the information about service capabilities and invocation mechanisms. P-WSDL is a lightweight WSDL extension for the description of performance characteristics of a web service. The approach is illustrated by use of an example application to a composite web service for travel planning

Web services are the building blocks of the emerging computing paradigm based on service-oriented architectures. A Web service is a self-describing, open component that supports rapid composition of distributed applications. Web service definitions are used to describe the service capabilities in terms of the operations of the service and the input and output messages for each operation. Such definitions are expressed in XML by use of the Web Service Definition Language (WSDL). Unfortunately, a WSDL description only addresses the functional aspects of a Web service without containing any useful description of non-functional or quality of service (QoS) characteristics. This paper introduces a lightweight WSDL extension for the description of QoS characteristics of a Web service. The extension is carried out as a metamodel transformation, according to principles and standards recommended by the model driven architecture (MDA). The WSDL metamodel is introduced and then transformed into the Q-WSDL (QoS-enabled WSDL) metamodel. The proposed Q-WSDL extension can effectively be used to specify QoS requirements, to establish service level agreements (SLA), to add QoS-oriented characteristics when querying registries of Web services and to support the automated mapping from WSDL documents to Q-WSDL ones and from UML models to Q-WSDL Web services

Industry 4.0 denotes a recent trend that aims at exploiting Cyber Physical Systems (CPS), based on IoT (Internet of Things) and cloud computing technologies, to obtain increased degrees of cooperation and communication in production systems, thus leading to what is referred to as Cyber Physical Production Systems (CPPS) or “Smart Factories”.

Healthcare organizations are increasingly pushed to improve the quality of care service taking into account the increasing complexity in patient treatment and the continuous reduction of available resources. The adoption of Business Process Management (BPM) practices is thus becoming a key enabler for the improvement of healthcare processes (HPs). Accordingly, methods and tools are required to address behavioral and performance aspects from the early phases of the process lifecycle in order to improve the quality of healthcare, reduce costly reworks and increase the effectiveness of BPM approaches. This paper specifically addresses the specification and analysis phases of the process lifecycle and introduces a model-driven method for healthcare process simulation. The proposed method is based on a model transformation approach that takes as input the process specification in BPMN, appropriately extended to include the performance properties of the process, and yields as output the corresponding process simulation code, ready to be executed. In order to illustrate the method and its effectiveness, the paper describes an example application to a process dealing with the hip fracture for elderly patients.