000165114 001__ 165114
000165114 005__ 20251212165958.0
000165114 0247_ $$2doi$$a10.1109/TMC.2023.3288604
000165114 0248_ $$2sideral$$a146517
000165114 037__ $$aART-2024-146517
000165114 041__ $$aeng
000165114 100__ $$aPerez-Valero, Jesus
000165114 245__ $$aEnergy-Aware Adaptive Scaling of Server Farms for NFV With Reliability Requirements
000165114 260__ $$c2024
000165114 5203_ $$aAuto-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.
000165114 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T31-23R$$9info: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$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101015956-Hexa-X$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131301B-I00$$9info:eu-repo/grantAgreement/ES/UZ/CUD2023-14$$9info:eu-repo/grantAgreement/ES/UZ/JIUZ-2022-IAR-08
000165114 540__ $$9info:eu-repo/semantics/closedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000165114 590__ $$a9.2$$b2024
000165114 591__ $$aTELECOMMUNICATIONS$$b9 / 120 = 0.075$$c2024$$dQ1$$eT1
000165114 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b9 / 258 = 0.035$$c2024$$dQ1$$eT1
000165114 592__ $$a2.332$$b2024
000165114 593__ $$aComputer Networks and Communications$$c2024$$dQ1
000165114 593__ $$aSoftware$$c2024$$dQ1
000165114 593__ $$aElectrical and Electronic Engineering$$c2024$$dQ1
000165114 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000165114 700__ $$aBanchs, Albert
000165114 700__ $$aSerrano, Pablo
000165114 700__ $$0(orcid)0000-0001-9052-9554$$aOrtín, Jorge
000165114 700__ $$aGarcia-Reinoso, Jaime
000165114 700__ $$aCosta-Pérez, Xavier
000165114 773__ $$g23, 5 (2024), 4273-4284$$pIEEE. Trans. Mob. Comput.$$tIEEE TRANSACTIONS ON MOBILE COMPUTING$$x1536-1233
000165114 8564_ $$s1212817$$uhttps://zaguan.unizar.es/record/165114/files/texto_completo.pdf$$yVersión publicada
000165114 8564_ $$s3885234$$uhttps://zaguan.unizar.es/record/165114/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000165114 909CO $$ooai:zaguan.unizar.es:165114$$particulos$$pdriver
000165114 951__ $$a2025-12-12-14:42:57
000165114 980__ $$aARTICLE