@conference {154, title = {Automated development of web-based modeling services for MSaaS platforms}, booktitle = {Proceedings of the Symposium on Model-driven Approaches for Simulation Engineering (Mod4Sim 2017) {\textendash} part of SpringSim 2017}, year = {2017}, publisher = {The Society for Modeling and Simulation International}, organization = {The Society for Modeling and Simulation International}, abstract = {

MSaaS (M\&S as a Service) is gaining momentum as an effective approach to bring the benefits of service-oriented architectures and cloud computing into the M\&S field, so as to enhance interoperability, composability, reusability and reduce the cost of M\&S efforts. Such significant advantages can be further enhanced by introducing automated model transformations that support the various phases of a M\&S effort, from simulation model building down to model implementation, deployment and execution. In previous contributions we have already addressed the use of automated model transformations that can be effectively adopted to provide simulation services for MSaaS platforms. This paper instead focuses on the automated development of modeling services for MSaaS, i.e., those services that allow platform users to easily build models in their own modeling language by use of a web-based user interface. Specifically, this work proposes an approach to automatically generate web-based visual editors from a metamodel that defines a given modeling language. Once generated, such editors can be made available on demand through a complete MSaaS platform, which also includes simulation services. The paper first describes the architecture of a MSaaS platform that includes modeling services, then illustrates the method for the automated development of web-based modeling services and, finally, gives a complete example application of the proposed method. {\textcopyright}2017 Society for Modeling \& Simulation International (SCS).

}, keywords = {Automation, Information services, Interoperability, Model driven development, Model transformation, Modeling languages, MSaaS, Reusability, Service oriented architecture (SOA), User interfaces, Visual editors, Visual languages, Web services, Web-based modeling, Websites}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020620129\&partnerID=40\&md5=d5baa89a6c01329dba3a44c6afbf1e92}, author = {Bocciarelli, P. and D{\textquoteright}Ambrogio, A. and Mastromattei, A. and Giglio, A.}, editor = {Durak U. and Cetinkaya D. and D{\textquoteright}Ambrogio A.} } @conference {150, title = {A PAAS-Based framework for automated performance analysis of service-oriented systems}, booktitle = {Proceedings - Winter Simulation Conference}, year = {2017}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, organization = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {

Service-oriented systems are often at the core of mission- or business-critical systems, and thus advanced quantitative analysis techniques are needed to assess, from the early development stages, whether or not the system accomplishes the stakeholder requirements and constraints. In this respect, in order to take advantage of the distributed nature of the considered systems, the use of distributed simulation (DS) appears the most natural and effective simulation approach. Nevertheless, the integration of traditional system development processes with DS approaches can be cost-And time-demanding. This paper presents SOAsim, a highly automated framework that allows system designers to generate the executable DS code from the model-based specification of the system under study, by use of automated model transformations. Moreover, in order to reduce the costs of setting-up dedicated DS platforms, SOAsim also automates the DS deployment and execution over a cloud-based infrastructure, according to a Platform-As-A-Service (PaaS) paradigm. {\textcopyright} 2016 IEEE.

}, keywords = {Automated model transformations, Automated performance analysis, Automation, Computer aided software engineering, Development stages, Distributed simulations, Model-based specifications, Platform as a Service (PaaS), Service Oriented Systems, Simulation approach, Traditional systems}, isbn = {9781509044863}, doi = {10.1109/WSC.2016.7822154}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014155535\&doi=10.1109\%2fWSC.2016.7822154\&partnerID=40\&md5=588d85610cba0fbef476d22e0f4a006f}, author = {D{\textquoteright}Ambrogio, A. and Bocciarelli, P. and Mastromattei, A.}, editor = {Roeder T.M., Szechtman R., Frazier P.I., Zhou E.} } @article {Bocciarelli2014573, title = {A model-driven method for enacting the design-time QoS analysis of business processes}, journal = {Software and Systems Modeling}, volume = {13}, number = {2}, year = {2014}, note = {cited By 6}, pages = {573-598}, publisher = {Springer Verlag}, abstract = {

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{\textquoteright}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. {\textcopyright} 2013 Springer-Verlag Berlin Heidelberg.

}, keywords = {Administrative data processing, Automation, Availability, BPMN, Business Process, Competition, Design, Enterprise resource management, Forecasting, High level languages, Information services, LQN, Mathematical models, MDA, Performance, Quality of service, Reliability, Service oriented architecture (SOA), Software architecture, Specifications, Web services}, issn = {16191366}, doi = {10.1007/s10270-013-0345-5}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899754418\&partnerID=40\&md5=ac7ae348f9d39ccb87a9aedb7d7524bd}, author = {Bocciarelli, P. and Andrea D{\textquoteright}Ambrogio} } @conference {D{\textquoteright}Ambrogio200544, title = {Performance model building of pervasive computing}, booktitle = {Proceedings - 2005 Workshop on Techniques, Methodologies and Tools for Performance Evaluation of Complex Systems, FIRB-Perf 2005}, volume = {2005}, year = {2005}, note = {cited By 3}, pages = {44-53}, abstract = {Performance model building is essential to predict the ability of an application to satisfy given levels of performance or to support the search for viable alternatives. Using automated methods of model building is becoming of increasing interest to software developers who have neither the skills nor the time to do it manually. This is particularly relevant in pervasive computing, where the large number of software and hardware components requires models of so large a size that using traditional manual methods of model building would be error prone and time consuming. This paper deals with an automated method to build performance models of pervasive computing applications, which require the integration of multiple technologies, including software layers, hardware platforms and wired/wireless networks. The considered performance models are of extended queueing network (EQN) type. The method is based on a procedure that receives as input the UML model of the application to yield as output the complete EQN model, which can then be evaluated by use of any evaluation tool. {\textcopyright} 2005 IEEE.}, keywords = {Automation, Computer software, Distributed computer systems, Extended queuing network (EQN), Manual control, Mathematical models, Performance models, Pervasive computing, Query languages, Software engineering, Wireless networks}, isbn = {0769524478; 9780769524474}, doi = {10.1109/FIRB-PERF.2005.15}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33846989003\&partnerID=40\&md5=3ba663ef9a7a1338b9485fda4973b320}, author = {Andrea D{\textquoteright}Ambrogio and Iazeolla, G.} }