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  1. Pubblicazioni

A matheuristic for green and robust 5G virtual network function placement

Contributo in Atti di convegno
Data di Pubblicazione:
2019
Citazione:
A matheuristic for green and robust 5G virtual network function placement / Bauschert, T.; D'Andreagiovanni, F.; Kassler, A.; Wang, C.. - 11454:(2019), pp. 430-438. ( 22nd International Conference on Applications of Evolutionary Computation, EvoApplications 2019, held as Part of EvoStar 2019 deu 2019) [10.1007/978-3-030-16692-2_29].
Abstract:
We investigate the problem of optimally placing virtual network functions in 5G-based virtualized infrastructures according to a green paradigm that pursues energy-efficiency. This optimization problem can be modelled as an articulated 0-1 Linear Program based on a flow model. Since the problem can prove hard to be solved by a state-of-the-art optimization software, even for instances of moderate size, we propose a new fast matheuristic for its solution. Preliminary computational tests on a set of realistic instances return encouraging results, showing that our algorithm can find better solutions in considerably less time than a state-of-the-art solver.
Tipologia CRIS:
Relazione in Atti di Convegno
Keywords:
5G; Matheuristic; Robust Optimization; Traffic uncertainty; Virtual Network Function
Elenco autori:
Bauschert, T.; D'Andreagiovanni, F.; Kassler, A.; Wang, C.
Autori di Ateneo:
D'ANDREAGIOVANNI FABIO
Link alla scheda completa:
https://iris.unimore.it/handle/11380/1388998
Link al Full Text:
https://iris.unimore.it//retrieve/handle/11380/1388998/936646/Bauschert.pdf
Titolo del libro:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pubblicato in:
LECTURE NOTES IN COMPUTER SCIENCE
Journal
LECTURE NOTES IN COMPUTER SCIENCE
Series
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