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An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters

This paper studies the problem of the dynamic scaling and load balancing of transparent virtualized network functions (VNFs). It analyzes different particularities of this problem, such as loop avoidance when performing scaling-out actions, and bidirectional flow affinity. To address this problem, a...

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Autores principales: Llorens-Carrodeguas, Alejandro, Leyva-Pupo, Irian, Cervelló-Pastor, Cristina, Piñeiro, Luis, Siddiqui, Shuaib
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703895/
https://www.ncbi.nlm.nih.gov/pubmed/34960377
http://dx.doi.org/10.3390/s21248283
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author Llorens-Carrodeguas, Alejandro
Leyva-Pupo, Irian
Cervelló-Pastor, Cristina
Piñeiro, Luis
Siddiqui, Shuaib
author_facet Llorens-Carrodeguas, Alejandro
Leyva-Pupo, Irian
Cervelló-Pastor, Cristina
Piñeiro, Luis
Siddiqui, Shuaib
author_sort Llorens-Carrodeguas, Alejandro
collection PubMed
description This paper studies the problem of the dynamic scaling and load balancing of transparent virtualized network functions (VNFs). It analyzes different particularities of this problem, such as loop avoidance when performing scaling-out actions, and bidirectional flow affinity. To address this problem, a software-defined networking (SDN)-based solution is implemented consisting of two SDN controllers and two OpenFlow switches (OFSs). In this approach, the SDN controllers run the solution logic (i.e., monitoring, scaling, and load-balancing modules). According to the SDN controllers instructions, the OFSs are responsible for redirecting traffic to and from the VNF clusters (i.e., load-balancing strategy). Several experiments were conducted to validate the feasibility of this proposed solution on a real testbed. Through connectivity tests, not only could end-to-end (E2E) traffic be successfully achieved through the VNF cluster, but the bidirectional flow affinity strategy was also found to perform well because it could simultaneously create flow rules in both switches. Moreover, the selected CPU-based load-balancing method guaranteed an average imbalance below 10% while ensuring that new incoming traffic was redirected to the least loaded instance without requiring packet modification. Additionally, the designed monitoring function was able to detect failures in the set of active members in near real-time and active new instances in less than a minute. Likewise, the proposed auto-scaling module had a quick response to traffic changes. Our solution showed that the use of SDN controllers along with OFS provides great flexibility to implement different load-balancing, scaling, and monitoring strategies.
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spelling pubmed-87038952021-12-25 An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters Llorens-Carrodeguas, Alejandro Leyva-Pupo, Irian Cervelló-Pastor, Cristina Piñeiro, Luis Siddiqui, Shuaib Sensors (Basel) Article This paper studies the problem of the dynamic scaling and load balancing of transparent virtualized network functions (VNFs). It analyzes different particularities of this problem, such as loop avoidance when performing scaling-out actions, and bidirectional flow affinity. To address this problem, a software-defined networking (SDN)-based solution is implemented consisting of two SDN controllers and two OpenFlow switches (OFSs). In this approach, the SDN controllers run the solution logic (i.e., monitoring, scaling, and load-balancing modules). According to the SDN controllers instructions, the OFSs are responsible for redirecting traffic to and from the VNF clusters (i.e., load-balancing strategy). Several experiments were conducted to validate the feasibility of this proposed solution on a real testbed. Through connectivity tests, not only could end-to-end (E2E) traffic be successfully achieved through the VNF cluster, but the bidirectional flow affinity strategy was also found to perform well because it could simultaneously create flow rules in both switches. Moreover, the selected CPU-based load-balancing method guaranteed an average imbalance below 10% while ensuring that new incoming traffic was redirected to the least loaded instance without requiring packet modification. Additionally, the designed monitoring function was able to detect failures in the set of active members in near real-time and active new instances in less than a minute. Likewise, the proposed auto-scaling module had a quick response to traffic changes. Our solution showed that the use of SDN controllers along with OFS provides great flexibility to implement different load-balancing, scaling, and monitoring strategies. MDPI 2021-12-11 /pmc/articles/PMC8703895/ /pubmed/34960377 http://dx.doi.org/10.3390/s21248283 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Llorens-Carrodeguas, Alejandro
Leyva-Pupo, Irian
Cervelló-Pastor, Cristina
Piñeiro, Luis
Siddiqui, Shuaib
An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters
title An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters
title_full An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters
title_fullStr An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters
title_full_unstemmed An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters
title_short An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters
title_sort sdn-based solution for horizontal auto-scaling and load balancing of transparent vnf clusters
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703895/
https://www.ncbi.nlm.nih.gov/pubmed/34960377
http://dx.doi.org/10.3390/s21248283
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