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Many-objective Placement Optimization in Virtual Network Functions

Volume 11, Issue 3, Page No 9–39, 2026

Author’s Name: Cristhian Jesus Benitez DominguezEmail, Fernando Ariel Britez CanteroEmail, Luis Guillermo Moré Rodríguez*Email, Diego Pedro Pinto-RoaEmail, Domingo Colbes SanabriaEmail
Facultad Politécnica, Universidad Nacional de Asunción, San Lorenzo, Paraguay
*whom correspondence should be addressed. E-mail: lmore@pol.una.py

Adv. Sci. Technol. Eng. Syst. J. 11(3), 9–39 (2026); crossref symbol DOI: 10.25046/aj110302

Keywords: NFV, VNF placement, MaOP, R-VEA, NSGA-III, MOEA/D, QoS, CAPEX, OPEX

Received: 29 April 2026, Revised: 5 June 2026, Accepted: 10 June 2026, Published Online: 24 June 2026
(This article belongs to the SP20 (Special Issue on Multidisciplinary Frontiers in Engineering, Computing and Applied Sciences 2026) & Section Network Engineering (ENW))
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Abstract

Network Functions Virtualization (NFV) poses the VNF placement problem under multiple, potentially conflicting objectives, such as Quality of Service (QoS), costs, and resource efficiency. This work treats VNF placement as a many-objective optimization problem (MaOP) and presents two primary contributions: (i) a correlation analysis of state-of-the-art objectives to reduce dimensionality while maintaining representativeness, resulting in the selection of 11 key objective functions — specifically, energy cost, bandwidth consumption, latency, traffic load, resource fragmentation, maximum link utilization, licensing costs, SLO costs, distance traveled, number of VNF instances, and effective throughput; and (ii) a systematic comparison of many-objective evolutionary algorithms (R-VEA, NSGA-III, and MOEA/D) across multiple topologies (ZIB54, INDIA, and EON) under various load levels. Results demonstrate through non-parametric Friedman and Wilcoxon statistical testing (adjusted 𝑝 < 0.05 via Holm-Bonferroni) that R-VEA achieves superior overall performance. Its Angle-Penalized Distance (APD) metric effectively balances convergence and diversity across most scenarios, producing a Vargha-Delaney effect size (𝐴12) greater than 0.86 in most topologies compared to NSGA-III and MOEA/D. Conversely, MOEA/D exhibits competitive performance only in specific high-traffic edge cases (e.g., ZIB54 under extreme load), where it outpaces R-VEA due to front distortion. Taken together, the findings indicate that while no single algorithm dominates in every outlier condition, R-VEA emerges as the most statistically consistent and robust solution for the 11-objective MaOP VNF placement problem. This contributes to ensuring QoS and reducing CAPEX/OPEX through high-performance solutions along the Pareto front.

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