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Model-driven Development of Virtual Network Embedding Algorithms with Model Transformation and Linear Optimization Techniques

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2018

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Gesellschaft für Informatik e.V.

Zusammenfassung

Enhancing the scalability and utilization of data centers, virtualization is a promising technology to manage, develop and operate network functions in a flexible way. For the placement of virtual networks in the data center, many approaches and algorithms are discussed in the literature to optimize solving the so-called virtual network embedding problem with respect to various optimization goals. This paper presents a new approach for the model-driven specification, simulation-based evaluation, and implementation of possible mapping algorithms that respect a set of given constraints and using linear optimization solving techniques to select one almost optimal mapping. Rule-based model transformation techniques are used to translate a given mapping problem into a linear optimization problem by taking domain specific knowledge into account. The resulting framework thus supports the design and evaluation of (correct-by-construction) virtual network embedding algorithms on a high level of abstraction. Well-defined model transformation rule refinement strategies can be used to reduce the search space for the employed linear optimization techniques.

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Tomaszek, Stefan; Leblebici, Erhan; Wang, Lin; Schürr, Andy (2018): Model-driven Development of Virtual Network Embedding Algorithms with Model Transformation and Linear Optimization Techniques. Modellierung 2018. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-674-9. pp. 39-54. Wissenschaftliche Beiträge. Braunschweig. 21.-23. Februar 2018

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