165114 20260217205445.0 doi 10.1109/TMC.2023.3288604 sideral 146517 ART-2024-146517 eng Perez-Valero, Jesus Energy-Aware Adaptive Scaling of Server Farms for NFV With Reliability Requirements 2024 Auto-scaling techniques aim to keep the right number of active servers for the current load: if this number is too small we risk service disruption, but if it is too large we waste resources. Despite the interest in the efficient operation of this type of systems, no prior work has addressed auto-scaling techniques for Network Function Virtualization (NFV) with stringent reliability requirements such as those envisioned in 5G (5 or 6 nines). To achieve such levels of reliability, we need to account for both the activation delay until servers become available (i.e., the wake-up or activation time) and the fallible nature of servers (which may fail with some probability). In this article, we build on control theory to design an auto-scaling technique for a server farm for NFV that guarantees certain reliability while minimizing the number of active resources. We show that the use of well-established tools from control theory results in convergence times much shorter than those obtained with state-of-the-art reinforcement learning techniques. This shows that, despite the current trend to apply machine learning to all sorts of networking problems, there may be some cases where other techniques (such as control theory) can be more suitable. info:eu-repo/grantAgreement/ES/DGA/T31-23R info:eu-repo/grantAgreement/EC/H2020/101015956/EU/ A flagship for B5G/6G vision and intelligent fabric of technology enablers connecting human, physical, and digital worlds /Hexa-X This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101015956-Hexa-X info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131301B-I00 info:eu-repo/grantAgreement/ES/UZ/CUD2023-14 info:eu-repo/grantAgreement/ES/UZ/JIUZ-2022-IAR-08 info:eu-repo/semantics/closedAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 9.2 2024 TELECOMMUNICATIONS 9 / 120 = 0.075 2024 Q1 T1 COMPUTER SCIENCE, INFORMATION SYSTEMS 9 / 258 = 0.035 2024 Q1 T1 2.332 2024 Computer Networks and Communications 2024 Q1 Software 2024 Q1 Electrical and Electronic Engineering 2024 Q1 11.9 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Banchs, Albert Serrano, Pablo Ortín, Jorge (orcid)0000-0001-9052-9554 Garcia-Reinoso, Jaime Costa-Pérez, Xavier 23, 5 (2024), 4273-4284 IEEE. Trans. Mob. Comput. IEEE TRANSACTIONS ON MOBILE COMPUTING 1536-1233 1212817 http://zaguan.unizar.es/record/165114/files/texto_completo.pdf Versión publicada 3885234 http://zaguan.unizar.es/record/165114/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:165114 articulos driver 2026-02-17-20:17:38 ARTICLE