(2025). Efficient Dynamic Hyperbolic Network Embeddings for Software-Defined Topologies. In 2025 IEEE Conference on Network Function Virtualization and Software-Defined Networking (NFV-SDN) (pp. 1–7). IEEE. https://doi.org/10.1109/nfv-sdn66355.2025.11349480
Περίληψη
Network embedding in Software Defined Networks and Network Function Virtualization is an evolving area, moving from static, rule-based methods to intelligent, adaptive algorithms for various functions and topology optimizations. As networks become more dynamic and service-oriented, embedding strategies must balance efficiency, scalability, and service quality, with Machine Leaning (ML) techniques playing an increasingly central role. In this paper, we focus on the network embedding itself and address how network embedding can tackle efficiently dynamic networks. More specifically, we propose ML-based approaches for embedding new nodes in an already embedded topology thus avoiding computational costs, while maintaining accuracy. The core idea of our approach (LHR) is to maintain the geometric consistency of the hyperbolic space while minimizing re-computation. Our evaluation results demonstrate the efficacy of our approach and set a basis for more advanced exploitation of ML in network embedding.
- DOI
- 10.1109/nfv-sdn66355.2025.11349480
- Τύπος
- Άρθρο σε Πρακτικά Συνεδρίου
- Έτος
- 2025
Σύνδεσμοι
BibTeX
@inproceedings{papoulia2025efficient,
title = {Efficient Dynamic Hyperbolic Network Embeddings for Software-Defined Topologies},
author = {Evgenia Papoulia and Vasileios Karyotis and Symeon Papavassiliou},
url = {https://doi.org/10.1109/nfv-sdn66355.2025.11349480},
doi = {10.1109/nfv-sdn66355.2025.11349480},
year = {2025},
date = {2025-01-01},
booktitle = {2025 IEEE Conference on Network Function Virtualization and Software-Defined Networking (NFV-SDN)},
pages = {1–7},
publisher = {IEEE},
abstract = {Network embedding in Software Defined Networks and Network Function Virtualization is an evolving area, moving from static, rule-based methods to intelligent, adaptive algorithms for various functions and topology optimizations. As networks become more dynamic and service-oriented, embedding strategies must balance efficiency, scalability, and service quality, with Machine Leaning (ML) techniques playing an increasingly central role. In this paper, we focus on the network embedding itself and address how network embedding can tackle efficiently dynamic networks. More specifically, we propose ML-based approaches for embedding new nodes in an already embedded topology thus avoiding computational costs, while maintaining accuracy. The core idea of our approach (LHR) is to maintain the geometric consistency of the hyperbolic space while minimizing re-computation. Our evaluation results demonstrate the efficacy of our approach and set a basis for more advanced exploitation of ML in network embedding.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}