ServiceNet: resource-efficient architecture for topology discovery in large-scale multi-tenant clouds
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Título: | ServiceNet: resource-efficient architecture for topology discovery in large-scale multi-tenant clouds |
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Autor/es: | Gama García, Ángel Manuel | Alcaraz Calero, José M. | Mora, Higinio | Wang, Qi |
Grupo/s de investigación o GITE: | Arquitecturas Inteligentes Aplicadas (AIA) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Topology discovery | Distributed computing | Resource management | Multi-tenancy | 5G networks |
Fecha de publicación: | 2024 |
Editor: | Springer Nature |
Cita bibliográfica: | Cluster Computing. 2024. https://doi.org/10.1007/s10586-024-04344-3 |
Resumen: | Modern computing infrastructures are evolving due to virtualisation, especially with the advent of 5G and future technologies. While this transition offers numerous benefits, it also presents challenges. Consequently, understanding these complex systems, including networks, services, and their interconnections, is crucial. This paper introduces ServiceNet, a groundbreaking architecture that accurately performs the important task of providing understanding of a multi-tenant architecture by discovering the complete topology, crucial in the realm of high-performance distributed computing. Experimental results have been carried out in different scenarios in order to validate our approach, demonstrating the effectiveness of our approach in comprehensive multi-tenant topology discovery. The experiments, involving up to forty tenant, highlight the adaptability of ServiceNet as a valuable tool for real-time monitoring in topology discovery purposes, even in challenging scenarios. |
Patrocinador/es: | European Commission Horizon 2020 5G-PPP Program under Grant Agreement Number: H2020-ICT-2020-2 / 101017226 “6G BRAINS: Bringing Reinforcement learning Into Radio Light Network for Massive Connections”. |
URI: | http://hdl.handle.net/10045/142150 |
ISSN: | 1386-7857 (Print) | 1573-7543 (Online) |
DOI: | 10.1007/s10586-024-04344-3 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1007/s10586-024-04344-3 |
Aparece en las colecciones: | Investigaciones financiadas por la UE INV - AIA - Artículos de Revistas |
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Gama-Garcia_etal_2024_ClusterComput.pdf | 2,26 MB | Adobe PDF | Abrir Vista previa | |
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