Safe Composition of Transformations

Heidenreich, Florian; Kopcsek, Jan; Aßmann, Uwe

doi:10.1007/978-3-642-13688-7_8

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…Extensive research has been carried out by the model transformations community on composing and executing transformation chains [4,20,28,45,46,56]. The Model Control Center (MCC) is Eclipse plugin for creating and executing transformation compositions [32].…”

Section: Model Management and Process Enactment Supportmentioning

confidence: 99%

Model-driven process enactment for NFV systems with MAPLE

et al. 2020

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The Network Functions Virtualization (NFV) advent is making way for the rapid deployment of network services (NS) for telecoms. Automation of network service management is one of the main challenges currently faced by the NFV community. Explicitly defining a process for the design, deployment, and management of network services and automating it is therefore highly desirable and beneficial for NFV systems. The use of model-driven orchestration means has been advocated in this context. As part of this effort to support automated process execution, we propose a process enactment approach with NFV systems as the target application domain. Our process enactment approach is megamodel-based. An integrated process modelling and enactment environment, MAPLE, has been built into Papyrus for this purpose. Process modelling is carried out with UML activity diagrams. The enactment environment transforms the process model to a model transformation chain, and then orchestrates it with the use of megamodels. In this paper we present our approach and environment MAPLE, its recent extension with new features as well as application to an enriched case study consisting of NS design and onboarding process.Keywords Process Enactment · Megamodelling · Papyrus · Network Functions Virtualization (NFV) IntroductionAutomating the end-to-end management of network services (NS), in other words, enacting the workflow or process for network service management without manual intervention is highly desirable in the telecommunications domain and remains a major challenge for network operators and service providers [11,38]. Network Function Virtualization (NFV) builds on cloud computing and has the ultimate goal of automating provisioning and management of network services -an essential feature for 5G systems. The European Telecom Standards Institute (ETSI) has recently launched a zero-touch network and service management group. As stated in [23], the challenges of 5G will trigger the need for a radical change in the way networks and services are managed and orchestrated.We believe the application of model-driven engineering (MDE) methods and tools is essential to further such developments in the NFV domain [8,31]. MDE advocates the use of models as first class citizens in the engineering process. The models are manipulated via transformations which form the backbone for automation in MDE. ETSI has recently released an information model for NFV [22]. Leveraging these models can substantially benefit the NFV systems by reducing their development and management efforts. Moreover, explicit modelling of the process not only allows for the automation of the NS management process but also paves the way for streamlining and optimization. Such a process model (PM) can potentially be mapped to model transformation chains hence enabling NS management and orchestration via model-driven process enactment [7,20,46]. Previously, we have proposed a model-based process for NS design and deployment [41]. The proposed workflow is compliant with the NFV reference f...

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Section: Model Management and Process Enactment Supportmentioning

confidence: 99%

Model-driven process enactment for NFV systems with MAPLE

et al. 2020

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“…Many efforts have been aimed at model transformation and composition [16][17][18][19][20]. An interesting work has been done in [17] to transform UML sequence diagrams into free choice Petri nets.…”

Section: Related Workmentioning

confidence: 99%

“…The authors have shown the partial ordered and concurrent behaviour of the diagrams naturally within the Petri net in a graphical way. An approach for the composition of transformation is done in [19] that guarantee the validity of the transformation chains. They have considered the transformation of UML class diagram to a relational model.…”

Section: Related Workmentioning

confidence: 99%

Transformation and composition of software design models for Model Driven Development

Meedeniya

Perera

Bowles

2015

2015 IEEE 10th International Conference on Industrial and Information Systems (ICIIS)

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Abstract-Software models play a significant role with the growth of software system development based on Model Driven Development (MDD) approach. Model transformations and compositions are the heart of MDD and allow the development of complex systems and their automated derivation. Moreover, software development of large and complex systems uses a collection of models, where model composition and decomposition are required. Various research studies have been done on specifying and executing MDD processes; however only a few of those have considered the validity of such transformations, thus safe composition and decomposition of models. This paper presents a general approach for model composition for the transformation from UML sequence diagrams to Coloured Petri Nets and validates the correctness of model composition using a mathematical proof. These transformations are based on formal rules, which have already been proven to be strongly consistent.

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“…This paper is an extended version of [HKA10] where we present TraCo, a Transformation Composition framework for safe composition of transformations. The goal is to allow the description, composition, and execution of heterogeneous transformation processes, while providing mechanisms for ensuring validity of these processes, which is checked both statically while developing MDSD processes and dynamically while executing them.…”

Section: Introductionmentioning

confidence: 99%