Virtual network embedding: Reducing the search space by model transformation techniques

Stefan Tomaszek*, Erhan Leblebici, Lin Wang, Andy Schürr

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


Virtualization is a promising technology to enhance the scalability and utilization of data centers for managing, developing, and operating network functions. Furthermore, it allows to flexibly place and execute virtual networks and machines on physical hardware. The problem of mapping a virtual network to physical resources, however, is known to be NP-hard and is often tackled by optimization techniques, e.g., by (ILP). On the one hand, highly tailored approaches based on heuristics significantly reduce the search space of the problem for specific environments and constraints, which, however, are difficult to transfer to other scenarios. On the other hand, ILP-based solutions are highly customizable and correct by construction with a huge search space. To mitigate search space problems while still guaranteeing correctness, we propose a combination of model transformation and ILP techniques. This combination is highly customizable and extensible in order to support multiple network domains, environments, and constraints allowing for rapid prototyping in different settings of virtualization tasks. Our experimental evaluation, finally, confirms that model transformation reduces the size of the optimization problem significantly and consequently the required runtime while still retaining the quality of mappings.

Original languageEnglish
Title of host publicationTheory and Practice of Model Transformation - 11th International Conference, ICMT 2018, Held as Part of STAF 2018, Proceedings
EditorsArend Rensink, Jesus Sanchez Cuadrado
PublisherSpringer Verlag
Number of pages17
ISBN (Print)9783319933160
Publication statusPublished - 1 Jan 2018
Externally publishedYes
Event11th International Conference on Model Transformation, ICMT 2018 Held as Part of STAF 2018 - Toulouse, France
Duration: 25 Jun 201826 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10888 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Model Transformation, ICMT 2018 Held as Part of STAF 2018


Acknowledgement. This work has been funded by the German Federal Ministry of Education and Research within the Software Campus project GraTraM at TU Darmstadt, funding code 01IS12054, and by the German Research Foundation (DFG) as part of project A1 within CRC 1053–MAKI.

FundersFunder number
Deutsche Forschungsgemeinschaft
Bundesministerium für Bildung und Forschung01IS12054


    • Data center
    • Integer linear programming
    • Model-driven development
    • Triple graph grammar
    • Virtual network embedding


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