Análisis de QoS para el Servicio de Videostreaming Implementando Segmentación de Red sobre una Red Móvil Basada en SD-RAN
Introducción: Las redes móviles de próxima generación requieren ser escalables y adaptables a los servicios de forma dinámica. Las redes definidas por software contribuyen a satisfacer estas necesidades mediante la “softwarización” de las funciones de red y la segmentación de red, en pro de la calid...
- Autores:
-
Campo, Wilmar
Chávez, Picón, Jose Luis
Chanchí Golondrino, Gabriel Elías
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2024
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13884
- Palabra clave:
- Quality of service
Mobile Network
Network Slicing
Video streaming
Calidad de servicio
red móvil
segmentación de red
videostreaming
- Rights
- openAccess
- License
- Inge CuC - 2024
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Análisis de QoS para el Servicio de Videostreaming Implementando Segmentación de Red sobre una Red Móvil Basada en SD-RAN |
| dc.title.translated.eng.fl_str_mv |
QoS Analysis for Video Streaming Service Implementing Network Slicing over a Mobile Network Based on SD-RAN |
| title |
Análisis de QoS para el Servicio de Videostreaming Implementando Segmentación de Red sobre una Red Móvil Basada en SD-RAN |
| spellingShingle |
Análisis de QoS para el Servicio de Videostreaming Implementando Segmentación de Red sobre una Red Móvil Basada en SD-RAN Quality of service Mobile Network Network Slicing Video streaming Calidad de servicio red móvil segmentación de red videostreaming |
| title_short |
Análisis de QoS para el Servicio de Videostreaming Implementando Segmentación de Red sobre una Red Móvil Basada en SD-RAN |
| title_full |
Análisis de QoS para el Servicio de Videostreaming Implementando Segmentación de Red sobre una Red Móvil Basada en SD-RAN |
| title_fullStr |
Análisis de QoS para el Servicio de Videostreaming Implementando Segmentación de Red sobre una Red Móvil Basada en SD-RAN |
| title_full_unstemmed |
Análisis de QoS para el Servicio de Videostreaming Implementando Segmentación de Red sobre una Red Móvil Basada en SD-RAN |
| title_sort |
Análisis de QoS para el Servicio de Videostreaming Implementando Segmentación de Red sobre una Red Móvil Basada en SD-RAN |
| dc.creator.fl_str_mv |
Campo, Wilmar Chávez, Picón, Jose Luis Chanchí Golondrino, Gabriel Elías |
| dc.contributor.author.spa.fl_str_mv |
Campo, Wilmar Chávez, Picón, Jose Luis Chanchí Golondrino, Gabriel Elías |
| dc.subject.eng.fl_str_mv |
Quality of service Mobile Network Network Slicing Video streaming |
| topic |
Quality of service Mobile Network Network Slicing Video streaming Calidad de servicio red móvil segmentación de red videostreaming |
| dc.subject.spa.fl_str_mv |
Calidad de servicio red móvil segmentación de red videostreaming |
| description |
Introducción: Las redes móviles de próxima generación requieren ser escalables y adaptables a los servicios de forma dinámica. Las redes definidas por software contribuyen a satisfacer estas necesidades mediante la “softwarización” de las funciones de red y la segmentación de red, en pro de la calidad de servicio (QoS). Sin embargo, existe una brecha investigativa sobre el comportamiento de las métricas de QoS para el servicio de videostreaming en entornos de segmentación de red. Objetivo: Analizar el comportamiento de las métricas de QoS, en el servicio de videostreaming soportado en el protocolo DASH, en escenarios de segmentos de red. Metodología: Para el desarrollo del presente artículo se definieron 3 fases que muestran la brecha investigativa, los experimentos y el análisis de resultados Resultados: Se construyeron 4 experimentos cada uno con 3 escenarios donde se varió la tasa de bit del servicio de videostreaming y la congestión del canal de comunicaciones. En cada caso se obtuvieron las métricas de rendimiento, retardo entre paquetes, variación del retardo y porcentaje de paquetes perdidos. Además, se analizó el comportamiento de cada métrica referente al impacto de las variaciones y respecto a los niveles recomendados para garantizar la QoS. Conclusiones: Para cada métrica se determina si cumple o no con los valores recomendados, identificando que la métrica más sensible es la variación del retardo, en segundo lugar, el porcentaje de pérdida de paquetes. Además, aunque las métricas de rendimiento y retardo entre paquetes se ven afectadas por las diferentes variaciones, cumplen con los valores establecidos. |
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2024 |
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2024-10-21 19:36:40 2024-12-06T08:30:15Z |
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2024-10-21 19:36:40 2024-12-06T08:30:15Z |
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2024-10-21 |
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Artículo de revista |
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Inge CuC |
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Cisco, “Cisco Annual Internet Report (2018–2023) White Paper,” 2023. [Online]. Available: https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html [2] Ericsson, “Ericsson Mobility Report 2021,” 2021. [Online]. Available: https://www.ericsson.com/en/mobility-report/reports/june-2020 [3] W. Y. Campo Muñoz, J. C. Imabachí Paz, and A. Escobar Zapata, “Algoritmo AAASH para el servicio de videostreaming sobre redes móviles,” Rev. Colomb. Tecnol. Av., vol. 1, no. 41, pp. 13–20, 2023. [4] M. Agiwal, A. Roy, and N. Saxena, “Next Generation 5G Wireless Networks: A Comprehensive Survey,” IEEE Commun. Surv. Tutorials, vol. 18, no. 3, pp. 1617–1655, 2016, doi: 10.1109/COMST.2016.2532458. [5] R. Vannithamby and S. Talwar, Towards 5G. Wiley & Sons, 2016. doi: 10.1002/9781118979846. [6] L. Bonati, M. Polese, S. D’Oro, S. Basagni, and T. Melodia, “Open, Programmable, and Virtualized 5G Networks: State-of-the-Art and the Road Ahead,” Comput. Networks, vol. 182, no. 9, p. 107516, May 2020, doi: 10.1016/j.comnet.2020.107516. [7] N. Slamnik-Krijestorac, H. Kremo, M. Ruffini, and J. M. Marquez-Barja, “Sharing Distributed and Heterogeneous Resources toward End-to-End 5G Networks: A Comprehensive Survey and a Taxonomy,” IEEE Commun. Surv. Tutorials, vol. 22, no. 3, pp. 1592–1628, 2020, doi: 10.1109/COMST.2020.3003818. [8] M. Condoluci and T. Mahmoodi, “Softwarization and virtualization in 5G mobile networks: Benefits, trends and challenges,” Comput. Networks, vol. 146, pp. 65–84, Dec. 2018, doi: 10.1016/j.comnet.2018.09.005. [9] R. Pereira and E. G. Pereira, “Video streaming:Overview and challenges in the internet of things,” in Pervasive Computing, Elsevier, 2016, pp. 417–444. doi: 10.1016/B978-0-12-803663-1.00013-9. [10] MPEG, “ISO/IEC 23009-1.” [Online]. Available: https://mpeg.chiariglione.org/tags/isoiec-23009-1 [11] ETSI, “TS 126 247 - V10.0.0 - Universal Mobile Telecommunications System (UMTS); LTE; Transparent end-to-end Packet-switched Streaming Service (PSS); Progressive Download and Dynamic Adaptive Streaming over HTTP (3GP-DASH) (3GPP TS 26.247 version 10.0.0 Release ,” 2011. [Online]. Available: https://www.etsi.org/deliver/etsi_ts/126200_126299/126247/10.00.00_60/ts_126247v100000p.pdf [12] W. Y. Campo, A. F. Escobar Zapata, and J. C. Imbachi Paz, “Análisis del servicio de video streaming basado en el algoritmo FDASH sobre LTE,” Cienc. e Ing. Neogranadina, vol. 29, no. 1, pp. 67–80, Aug. 2019, doi: 10.18359/rcin.3122. [13] B.-S. Paul Lin, Y.-B. Lin, L.-P. Tung, and F. Joseph Lin, “Exploring Network Softwarization and Virtualization by Applying SDN/NFV to 5G and IoT,” Trans. Networks Commun., vol. 6, no. 4, pp. 1–13, Aug. 2018, doi: 10.14738/tnc.64.4825. [14] G. C. Valastro, D. Panno, and S. Riolo, “A SDN/NFV based C-RAN architecture for 5G Mobile Networks,” in 2018 International Conference on Selected Topics in Mobile and Wireless Networking (MoWNeT), Jun. 2018, pp. 1–8. doi: 10.1109/MoWNet.2018.8428882. [15] M. A., A. Mohammed, and M. Yamani, “A Brief Survey on 5G Wireless Mobile Network,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 11, 2017, doi: 10.14569/IJACSA.2017.081107. [16] G. Sun, F. Liu, J. Lai, and G. Liu, “Software Defined Wireless Network Architecture for the Next Generation Mobile Communication: Proposal and Initial Prototype,” J. Commun., vol. 9, no. 12, pp. 946–953, 2014, doi: 10.12720/jcm.9.12.946-953. [17] N. Nikaein, “Slicing and Orchestration in Service-Oriented 5G Networks - Keynotes at IEEE CAMAD,” 2018. [Online]. Available: https://wp-files.comsoc.org/camad-2018/files/2018/10/CAMAD_KN_nikaein_2018_v2.0.pdf [18] P. Rost et al., “Network Slicing to Enable Scalability and Flexibility in 5G Mobile Networks,” IEEE Commun. Mag., vol. 55, no. 5, pp. 72–79, May 2017, doi: 10.1109/MCOM.2017.1600920. [19] A. A. Barakabitze, A. Ahmad, R. Mijumbi, and A. Hines, “5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges,” Comput. Networks, vol. 167, p. 106984, Feb. 2020, doi: 10.1016/j.comnet.2019.106984. [20] X. Foukas, G. Patounas, A. Elmokashfi, and M. K. Marina, “Network Slicing in 5G: Survey and Challenges,” IEEE Commun. Mag., vol. 55, no. 5, pp. 94–100, May 2017, doi: 10.1109/MCOM.2017.1600951. [21] S. Zhang, “An Overview of Network Slicing for 5G,” IEEE Wirel. Commun., vol. 26, no. 3, pp. 111–117, Jun. 2019, doi: 10.1109/MWC.2019.1800234. [22] A. N. Toosi, R. Mahmud, Q. Chi, and R. Buyya, “Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds,” in Fog and Edge Computing, Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019, pp. 79–101. doi: 10.1002/9781119525080.ch4. [23] S. El Hassani, A. Haidinel, and H. Jebbar, “Road to 5G: Key Enabling Technologies,” J. Commun., vol. 14, no. 11, pp. 1034–1048, 2019, doi: 10.12720/jcm.14.11.1034-1048. [24] P. Salva-Garcia, J. M. Alcaraz-Calero, Q. Wang, M. Barros, and A. Gavras, “Real-Time Video Adaptation in Virtualised 5G Networks,” in 2019 IEEE 44th Conference on Local Computer Networks (LCN), Oct. 2019, pp. 214–217. doi: 10.1109/LCN44214.2019.8990815. [25] C. Baena, S. Fortes, E. Baena, and R. Barco, “Estimation of Video Streaming KQIs for Radio Access Negotiation in Network Slicing Scenarios,” IEEE Commun. Lett., vol. 24, no. 6, pp. 1304–1307, Jun. 2020, doi: 10.1109/LCOMM.2020.2979713. [26] Q. Wang et al., “Enable Advanced QoS-Aware Network Slicing in 5G Networks for Slice-Based Media Use Cases,” IEEE Trans. Broadcast., vol. 65, no. 2, pp. 444–453, Jun. 2019, doi: 10.1109/TBC.2019.2901402. [27] S. Dahmen-Lhuissier, “ETSI - Multi-access Edge Computing - Standards for MEC,” 2020. [Online]. Available: https://www.etsi.org/technologies/multi-access-edge-computing [28] J. Wang, J. Weitzen, O. Bayat, V. Sevindik, and M. Li, “Performance Model for Video Service in 5G Networks,” Futur. Internet, vol. 12, no. 6, p. 99, Jun. 2020, doi: 10.3390/fi12060099. [29] N. Ganesan, Vivek, and B. Thangaraju, “Pox Controller based Qos Evaluation for 5G Systems-Network Slicing,” in 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), Feb. 2020, pp. 394–398. doi: 10.1109/SPIN48934.2020.9071353. [30] Q. Ou et al., “A Method of Dynamic Resource Adjustment for 5G Network Slice,” Adv. Intell. Syst. Comput., vol. 1143, pp. 929–936, 2021, doi: 10.1007/978-981-15-3753-0_91. [31] Y. Sun, M. Peng, S. Mao, and S. Yan, “Hierarchical Radio Resource Allocation for Network Slicing in Fog Radio Access Networks,” IEEE Trans. Veh. Technol., vol. 68, no. 4, pp. 3866–3881, Apr. 2019, doi: 10.1109/TVT.2019.2896586. [32] K. S. Ibarra-Lancheros, G. Puerto-Leguizamón, and C. Suárez-Fajardo, “Quality of service evaluation based on network slicing for software-defined 5G systems,” TecnoLógicas, vol. 21, no. 43, pp. 27–41, Sep. 2018, doi: 10.22430/22565337.1066. [33] C. F. Müller, G. Galaviz, Á. G. Andrade, I. Kaiser, and W. Fengler, “Evaluation of Scheduling Algorithms for 5G Mobile Systems,” in Studies in Systems, Decision and Control, 2018, pp. 213–233. doi: 10.1007/978-3-319-74060-7_12. [34] J. L. Chavez, W. Y. Campo, and G. E. Chanchí, “Construcción de un banco de pruebas para redes 5G basado en SDN y SDR,” Rev. Ibérica Sist. e Tecnol. Informação, no. E42, pp. 425–437, 2021. [35] R. Kumar, Research methodology a step by step guide for beginners. Los Angeles. USA: SAGE, 2011. [Online]. Available: http://www.sociology.kpi.ua/wp-content/uploads/2014/06/Ranjit_Kumar-Research_Methodology_A_Step-by-Step_G.pdf [36] J. L. Chavez and W. Campo, “Análisis de QoS para el servicio de videostreaming implementando segmentación de recursos sobre una red móvil basada en SD-RAN,” Universidad del Quindío, 2022. [37] TSGR, “ETSI TS 136 213 - V17.1.0 - LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures (3GPP TS 36.213 version 17.1.0 Release 17).” 2022. [Online]. Available: https://portal.etsi.org/TB/ETSIDeliverableStatus.aspx [38] DASHIF, “DASH Industry Forum | Catalyzing the adoption of MPEG-DASH.” [Online]. Available: https://dashif.org/about/ [39] CISCO, “Video Quality of Service (QOS).” 2017. [Online]. Available: https://www.cisco.com/c/en/us/support/docs/quality-of-service-qos/qos-video/212134-Video-Quality-of-Service-QOS-Tutorial.html |
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Campo, WilmarChávez, Picón, Jose LuisChanchí Golondrino, Gabriel Elías2024-10-21 19:36:402024-12-06T08:30:15Z2024-10-21 19:36:402024-12-06T08:30:15Z2024-10-210122-6517https://hdl.handle.net/11323/13884https://doi.org/10.17981/ingecuc.20.2.2024.0510.17981/ingecuc.20.2.2024.052382-4700Introducción: Las redes móviles de próxima generación requieren ser escalables y adaptables a los servicios de forma dinámica. Las redes definidas por software contribuyen a satisfacer estas necesidades mediante la “softwarización” de las funciones de red y la segmentación de red, en pro de la calidad de servicio (QoS). Sin embargo, existe una brecha investigativa sobre el comportamiento de las métricas de QoS para el servicio de videostreaming en entornos de segmentación de red. Objetivo: Analizar el comportamiento de las métricas de QoS, en el servicio de videostreaming soportado en el protocolo DASH, en escenarios de segmentos de red. Metodología: Para el desarrollo del presente artículo se definieron 3 fases que muestran la brecha investigativa, los experimentos y el análisis de resultados Resultados: Se construyeron 4 experimentos cada uno con 3 escenarios donde se varió la tasa de bit del servicio de videostreaming y la congestión del canal de comunicaciones. En cada caso se obtuvieron las métricas de rendimiento, retardo entre paquetes, variación del retardo y porcentaje de paquetes perdidos. Además, se analizó el comportamiento de cada métrica referente al impacto de las variaciones y respecto a los niveles recomendados para garantizar la QoS. Conclusiones: Para cada métrica se determina si cumple o no con los valores recomendados, identificando que la métrica más sensible es la variación del retardo, en segundo lugar, el porcentaje de pérdida de paquetes. Además, aunque las métricas de rendimiento y retardo entre paquetes se ven afectadas por las diferentes variaciones, cumplen con los valores establecidos.Introduction: Next-generation mobile networks need to be scalable, and dynamically adaptable to services. Software-defined networking (SDN) technology contributes to satisfy these needs through the softwarization of network functions and network slicing in favor of Quality of Service (QoS). However, there is a research gap on the behavior of QoS metrics for video streaming service in network slicing environments. Objective: To analyze the behavior of QoS metrics in the video streaming service supported by the DASH protocol in network slicing scenarios. Method: For the development of this paper, 3 phases were defined to show the research gap, the experiments and the analysis of results. Results: Four experiments were built, each with three scenarios where the bitrate of the videostreaming service and the congestion of the communications channel were varied. In each case, throughput, inter-packet delay, jitter and packet loss percentage metrics were obtained. In addition, the performance of each metric was analyzed with respect to the impact of variations and with respect to the recommended levels to guarantee QoS. Conclusions: For each metric, it was determined whether it complies with the recommended values, identifying that the most sensitive metric is jitter, followed by packet loss percentage. Similarly, although the throughput and inter-packet delay metrics are affected by variations, they comply with the established values.application/pdfengUniversidad de la CostaInge CuC - 2024http://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistascientificas.cuc.edu.co/ingecuc/article/view/5270Quality of serviceMobile NetworkNetwork SlicingVideo streamingCalidad de serviciored móvilsegmentación de redvideostreamingAnálisis de QoS para el Servicio de Videostreaming Implementando Segmentación de Red sobre una Red Móvil Basada en SD-RANQoS Analysis for Video Streaming Service Implementing Network Slicing over a Mobile Network Based on SD-RANArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Inge CuCCisco, “Cisco Annual Internet Report (2018–2023) White Paper,” 2023. 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Networks, vol. 182, no. 9, p. 107516, May 2020, doi: 10.1016/j.comnet.2020.107516. [7] N. Slamnik-Krijestorac, H. Kremo, M. Ruffini, and J. M. Marquez-Barja, “Sharing Distributed and Heterogeneous Resources toward End-to-End 5G Networks: A Comprehensive Survey and a Taxonomy,” IEEE Commun. Surv. Tutorials, vol. 22, no. 3, pp. 1592–1628, 2020, doi: 10.1109/COMST.2020.3003818. [8] M. Condoluci and T. Mahmoodi, “Softwarization and virtualization in 5G mobile networks: Benefits, trends and challenges,” Comput. Networks, vol. 146, pp. 65–84, Dec. 2018, doi: 10.1016/j.comnet.2018.09.005. [9] R. Pereira and E. G. Pereira, “Video streaming:Overview and challenges in the internet of things,” in Pervasive Computing, Elsevier, 2016, pp. 417–444. doi: 10.1016/B978-0-12-803663-1.00013-9. [10] MPEG, “ISO/IEC 23009-1.” [Online]. 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