Safe Composition of Transformations.

Heidenreich, Florian; Kopcsek, Jan; Aßmann, Uwe

doi:10.5381/jot.2011.10.1.a7

Cited by 10 publications

(4 citation statements)

References 7 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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“…Other modelling environments have similar tools available that can be used to setup such an orchestration, e.g. [240,145,131].…”

Section: Orchestrating Processesmentioning

confidence: 99%

Foundations of Multi-Paradigm Modelling for Cyber-Physical Systems

Broenink¹

2020

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sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

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“…The intent is not only to save effort but also to ensure that knowledge is propagated correctly and completely from those that create it to those that require it. Making sure that transformations are correctly chained or composed is a topic that deserves further attention [8,9].…”

Section: Fine-tuning Transformationsmentioning

confidence: 99%

Fine-Tuning Model Transformation: Change Propagation in Context of Consistency, Completeness, and Human Guidance

Egyed

Demuth

Ghabi

et al. 2011

Theory and Practice of Model Transformations

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An important role of model transformation is in exchanging modeling information among diverse modeling languages. However, while a model is typically constrained by other models, additional information is often necessary to transform said models entirely. This dilemma poses unique challenges for the model transformation community. To counter this problem we require a smart transformation assistant. Such an assistant should be able to combine information from diverse models, react incrementally to enable transformation as information becomes available, and accept human guidancefrom direct queries to understanding the designer(s) intentions. Such an assistant should embrace variability to explicitly express and constrain uncertainties during transformation -for example, by transforming alternatives (if no unique transformation result is computable) and constraining these alternatives during subsequent modeling. We would want this smart assistant to optimize how it seeks guidance, perhaps by asking the most beneficial questions first while avoiding asking questions at inappropriate times. Finally, we would want to ensure that such an assistant produces correct transformation results despite the presence of inconsistencies. Inconsistencies are often tolerated yet we have to understand that their presence may inadvertently trigger erroneous transformations, thus requiring backtracking and/or sandboxing of transformation results. This paper explores these and other issues concerning model transformation and sketches challenges and opportunities.

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Safe Composition of Transformations.

Cited by 10 publications

References 7 publications

Model-driven process enactment for NFV systems with MAPLE

Model-driven process enactment for NFV systems with MAPLE

Foundations of Multi-Paradigm Modelling for Cyber-Physical Systems

Fine-Tuning Model Transformation: Change Propagation in Context of Consistency, Completeness, and Human Guidance

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