The analysis of modern business processes implemented as orchestration of software services demands for new approaches that explicitly take into account the inherent complexity and distribution characteristics of such processes. In this respect, Distributed Simulation (DS) offers a viable tool to cope with such a demand, due to the aggregation, scalability, representativeness and load balancing properties that it allows to achieve. However, the use of DS is mostly limited by the specialized technical know-how and the extra-development that DS requires with respect to approaches based on conventional local simulation. This paper proposes a model-driven method that enables the DS-based analysis of business processes by introducing the automated transformation of business process models into analysis models that are specified as Extended Queueing Network (EQN) models and executed as distributed simulations. The paper also presents an example application to a business process for an e-commerce scenario.
The increasing complexity of modern systems makes their design, development, and operation extremely challenging and therefore new systems engineering and modeling and simulation (M&S) methods, techniques, and tools are emerging, also to benefit from distributed simulation environments. In this context, one of the most mature and popular standards for distributed simulation is the IEEE 1516-2010 - Standard for M&S high level architecture (HLA). However, building and maintaining distributed simulations components, based on the IEEE 1516-2010 standard, is still a challenging and effort-consuming task. To ease the development of full-fledged HLA-based simulations, the paper proposes the MONADS method (MOdel-driveN Architecture for Distributed Simulation), which relies on the model-driven systems engineering paradigm. The method takes as input system models specified in Systems Modeling Language, the reference modeling language in the systems engineering field, and produces as output the final code of the corresponding HLA-based distributed simulation through a chain of model-to-model and model-to-text transformations. The obtained simulation code is based on the HLA Development Kit software framework, which has been developed by the SMASH-Lab (System Modeling and Simulation Hub - Laboratory) of the University of Calabria (Italy), in cooperation with the Software, Robotics, and Simulation Division (ER) of NASA’s Lyndon B. Johnson Space Center (JSC) in Houston (TX, USA). The effectiveness of the method is shown through a case study that concerns a military patrol operation, in which a set of drones are engaged to patrol the border of a military area, in order to prevent both ground and flight attacks from entering the area.
The development of complex systems requires the use of quantitative analysis techniques to allow a designtime evaluation of the system behavior. In this context, distributed simulation (DS) techniques can be effectively introduced to assess whether or not the system satisfies the user requirements. Unfortunately, the development of a DS requires the availability of an IT infrastructure that could not comply with time-to-market requirements and budget constraints. In this respect, this work introduces HLAcloud, a model-driven and cloud-based framework to support both the implementation of a DS system from a SysML specification of the system under study and its execution over a public cloud infrastructure. The proposed approach, which exploits the HLA (High Level Architecture) DS standard, is founded on the use of model transformation techniques to generate both the Java/HLA source code of the DS system and the scripts required to deploy and execute the HLA federation onto the PlanetLab cloud-based infrastructure
Simulation is a key technique for enabling business process analysts to predict the process behavior at design time. However, some issues limit the effectiveness of business process simulation (e.g., lack of simulation know how, costs and difficulties for gathering process data, semantic gap between the business process model and the simulation model). This paper proposes a model-driven method that automates the generation of executable business process simulation code. In order to address the increasing complexity and to take into account the inherent collaborative aspects of modern business processes, the simulation code produced by the proposed method replicates the business process distributed structure (in terms, e.g., of a service-oriented architecture) by including a set of simulation services that are orchestrated into a distributed simulation execution. The characterization of business processes in terms of the required performance properties is introduced through standard BPMN annotations according to a well-defined syntax, thus avoiding the need of additional languages. The implementation of the executable simulation code is based on the eBPMN language, a domain-specific language that preserves the semantic behavior of the original BPMN standard
Business Process Management (BPM) is a holistic approach for describing, analyzing, executing, managing, and improving large enterprise business processes. A business process can be seen as a flow of tasks that are orchestrated to accomplish well-defined goals such as goods production or services delivery. From an IT perspective, BPM is closely related to a business process automation approach carried out by use of IT standards and technologies, such as service-oriented architectures (SOAs) and Web Services. This paper specifically focuses on fully automated business processes that are defined and executed as orchestrations of software services. In a BPM context, the ability to predict at design time the business process behavior assumes a strategic relevance, both to early assess whether or not the business goals are achieved and to gain a competitive advantage. A business process is typically specified by use of Business Process Modeling Notation (BPMN), the standard language for the high-level description of business processes. Unfortunately, BPMN does not support the characterization of the business process in terms of nonfunctional or QoS properties, such as performance and reliability. To overcome such a limitation, this paper introduces Performability-enabled BPMN (PyBPMN), a lightweight BPMN extension for the specification of performance and reliability properties. PyBPMN enables the design time prediction of the business processes behavior, in terms of performance and reliability properties. Such prediction activity requires the use of models that are to be first built and then evaluated. In this respect, this work introduces a model-driven method that exploits PyBPMN to predict, at design time, the performance and the reliability of a business process, either to select the process configuration that provides the best behavior or to check if a given configuration satisfies the overall requirements. The proposed model-driven method that enacts the automated analysis of a business process behavior embraces the complete business process development cycle, from the specification phase down to the implementation phase. The paper also describes how the proposed model-driven method is implemented. The several model transformations at the core of the method have been implemented by use of QVT, and the standard language for specifying model transformations provided by OMG's MDA. The availability of such automated model transformations allows business analysts to predict the process behavior with no extra effort and without being required to own specific skills of performance or reliability theory, as shown by use of an example application
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