Modelo de coordinación para la actividad autorregulada en redes definidas por software
graficas, tablas
- Autores:
-
Aristizábal Quintero, Luz Angela
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/83450
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
Redes definidas por software
Procesamiento de señales gráficas
Aprendizaje por refuerzo
Autorregulación
Software defined networks
Graphical signal processing
Self-regulated.
Reinforcement learning
Análisis de redes
Network analysis
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Modelo de coordinación para la actividad autorregulada en redes definidas por software |
| dc.title.translated.eng.fl_str_mv |
Coordination model for the self-regulated activity of software-defined networks |
| title |
Modelo de coordinación para la actividad autorregulada en redes definidas por software |
| spellingShingle |
Modelo de coordinación para la actividad autorregulada en redes definidas por software 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Redes definidas por software Procesamiento de señales gráficas Aprendizaje por refuerzo Autorregulación Software defined networks Graphical signal processing Self-regulated. Reinforcement learning Análisis de redes Network analysis |
| title_short |
Modelo de coordinación para la actividad autorregulada en redes definidas por software |
| title_full |
Modelo de coordinación para la actividad autorregulada en redes definidas por software |
| title_fullStr |
Modelo de coordinación para la actividad autorregulada en redes definidas por software |
| title_full_unstemmed |
Modelo de coordinación para la actividad autorregulada en redes definidas por software |
| title_sort |
Modelo de coordinación para la actividad autorregulada en redes definidas por software |
| dc.creator.fl_str_mv |
Aristizábal Quintero, Luz Angela |
| dc.contributor.advisor.none.fl_str_mv |
Toro García, Nicolás |
| dc.contributor.author.none.fl_str_mv |
Aristizábal Quintero, Luz Angela |
| dc.contributor.researchgroup.spa.fl_str_mv |
Grupo de Investigación en Recursos Energéticos Gire |
| dc.contributor.orcid.spa.fl_str_mv |
Aristizábal Quintero, Luz Angela [0000-0003-4510-9029] |
| dc.contributor.cvlac.spa.fl_str_mv |
Aristizábal Quintero, Luz Angela [0000219169] |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación |
| topic |
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Redes definidas por software Procesamiento de señales gráficas Aprendizaje por refuerzo Autorregulación Software defined networks Graphical signal processing Self-regulated. Reinforcement learning Análisis de redes Network analysis |
| dc.subject.proposal.spa.fl_str_mv |
Redes definidas por software Procesamiento de señales gráficas Aprendizaje por refuerzo Autorregulación |
| dc.subject.proposal.eng.fl_str_mv |
Software defined networks Graphical signal processing Self-regulated. Reinforcement learning |
| dc.subject.unesco.spa.fl_str_mv |
Análisis de redes |
| dc.subject.unesco.eng.fl_str_mv |
Network analysis |
| description |
graficas, tablas |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2023-02-13T14:42:53Z |
| dc.date.available.none.fl_str_mv |
2023-02-13T14:42:53Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
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info:eu-repo/semantics/doctoralThesis |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_db06 |
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Image Text |
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https://repositorio.unal.edu.co/handle/unal/83450 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/83450 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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ACI. (s.f.). ACI policy Model.Recuperado de https://sandboxapicdc.cisco.com/help/content/index.html#c_about-cisco-aci.html. Adami, D. (2015). A Network Control Application enabling Software-defined Quality of Service. IEEE Xplore. Adrichem, N.V. Doerr, C.(2014) OpenNetMon: Network Monitoring in Openflow Software-Defined Networks. IEEE Network Operations and Management Symposium (NOMS), IEEE Xplore, 1-8. Akash, S., Varalakshmi, P., & Somasundaram, V. (2018). Congestion Control Mechanism in Software Defined Networking by Traffic Rerouting. IEEE Xplore. Ryan, G., & Tischer, J. (2017). Programming Automatic Cisco Networks. Cisco Press. ACI. (n.d.). ACI policy Model. https://sandboxapicdc.cisco.com/help/content/index.html#c_about-cisco-aci.html. Adami, D., & Donatini, L. (2015). A Network Control Application enabling Software-defined Quality of Service. IEEE Xplore. Astuto, B., Mendonca, M., & Nguy, X. (2014). A Survey of Software-Defined Networking: Past, Present. and Future of Programmable Networks. IEEE Communications Surveys and Tutorials 16 (3)., 1617 - 1634. Bailey, S., Balsal, D., & Dunbar, L. (2014). SDN Architecture Overview. Open Network Foundation. https://opennetworking.org/wp-content/uploads/2013/02/SDN-architecture-overview-1.0.pdf. Balasubramaniam, S., & Botvich, D. (2006). Applying Blood Glucose Homeostatic Model Towards Self-management of IP QoS Provisioned Networks. Autonomic Principles of IP Operations and Management. Springer, pp 84–95. Bocaletti, S., Bianconi, G., Criado, R., & Del Genio, C. (2014). The structure and dynamics of multilayer networks. Elsevier. Volume 544, Issue 1, 1-122. Ciancarini, P. (1996). Coordination models and languages as software integrators. ACM Computing Surveys, vol. 28, no. 2, 300–. CISCO. (2018). Indice de redes Visuales. https://newsroom.cisco.com/press-release-content?articleId=1955935. Hammond, D., Vandergheynst, P., & Gribonw, R. (2019). The Spectral Graph Wavelet Transform: Fundamental Theory and Fast Computation of Graph Signals. Springer International Publishing, 141-175. Hommond, D., Vandergheynst, P., & Gribonw, R. (2011). Wavelets on Graphs via Spectral Graph Theory. Applied and Computational Harmonic Analysis, Elsevier 30 (2), 129–150. Irion, J. (2015). Multiescale Transforms for signal on graph: Method and applications. University of California, Davis. James, R. K. (2017). Computer Networking: A Top-Down Approach . Pearson. Jammal, M., Singh, T., Shami, A., Asal, R., & Li, Y. (2014). Software defined networking: State of the art and research challenges. Computer Networks, vol. 72, 74-98. Kathiravelu, P. (2016). Software-Defined Networking-Based Enhancements to Data Quality and QoS in Multi-Tenanted Data Center Clouds. IEEE Xplore. Kobayashi, M., Seetharaman, S., & Parulka, G. (2014). Maturing of OpenFlow and Software-defined Networking through deployments. Computer Networks. 61, 151–175. Kurose, J. F. (2020). Computer Networking . Pearson. Lara, A., & Kolasani , A. (2014). Network Innovation using OpenFlow: A Survey. IEEE Communications Surveys & Tutorials, Vol. 16, No. 3. Latif, Z., Sharif, K., Li, F., & Monjurul, K. (2020). A comprehensive survey of interface protocols for software defined networks. Journal of Network and Computer Applications. Laurençon, H., Ségerie, C.-R., & Lu, J. (2021). Continuous Homeostatic Reinforcement Learning for Self-Regulated Autonomous Agents. Cornell University, https://arxiv.org/abs/2109.06580. Luong, D., Outtagarts, A., & Hebbar, A. (2016). Traffic Monitoring in Software Defined Netwoks Using Opendaylight Controller. Lecture Notes in Computer Science, LNCS, Vol. 10026, Springer., pp 38-48. Ma , J., Huang, W., & Segarra, S. (2016). Difusión Filtering of Graph Signal and its Use in Recomendation Systems. IEEExplore. Manar, J., Taranpreet, S., & Abdallah, S. (2014). Software defined networking: State of the art and research challenges. Computer Network. Martolia, K. A. (2016). Congestion Control Techniques. International Conference on Communication and Signal Processing (ICCSP). IEEE Xplore. Mckeown, N., Balakrishnan , G., Parulkar, L., & Peterson, J. (2008). Openflow: Enabling innovation in campus networks. SIGCOMM CCR, Vol. 38, Nro. 2., 69-74. Mizuki, O., & Ikegami, A. (2014). Dynamic homeostasis in packet switching networks. International. Society for Adaptive Behavior. N.L.M. van Adrichem, C. D., van Adriechem , N., Doerr, C., & Kuiper, F. (2014). OpenNetMon: Network Monitoring in Openflow Software-Defined Networks. IEEE Network Operations and Management Symposium (NOMS), IEEE Xplore, 1-8. Omicini, A. (2013). Nature-Inspired Coordination Models: Current Status and future trends. ISRN Software Engineering. ON-LINE. (2014). Open Flow. https://www.opennetworking.org/wp-content/uploads/2014/10/openflow-switch-v1.5.1.pdf. openflex. (n.d.). OpenFlex vs Openflow. https://www.cisco.com/c/en/us/solutions/collateral/data-center-virtualization/application-centric-infrastructure/white-paper-c11-731302.html. Pérez, D. M. (2018). Administración y seguridad en redes de computadoras. . Alfa-Omega . Perumal, S., & Varalakshmi, S. (2017). Research Confront in Software Defined Networking Environment: A Survey. Springer Communications in Computer and Information Science. Przemysław Ignaciuk, A. B. (2013). Congestion Control in Data Transmission Networks. Springer. Qiang, H., Min, H., Xiangyi, C., & Wang, X. (2020). Prediction for Energy Efficiency Optimization in Software-Defined Networking. IEEE Xplore. Ren, C. W., & Yongcan. (2011). Distributed Coordination of multiagent networks. Springer-Verlag. Sandryhaila, A., & Moura, J. (2013). Discrete Signal Processing on Graphs. IEEE TRANSACTIONS ON SIGNAL PROCESSING. SevOne. (n.d.). Software Defined Network Monitoring. https://www.sevone.com/wp-content/uploads/2021/04/SDN_Solutions_Guide-1.pdf. Shumman, D., Narang, S., Frossard, P., & Ortega, A. (2013). The Emerging Field of Signal Processing On Graphs: Extending High- Dimensional Data Analysis to Networks and Other Irregular Domains. IEEE Signal processing Magazine, 83 – 98. Sifakis, J. (2019). Autonomous Systems – An Architectural. In F. C. Michele Loreti, Models, Languages, and Tools (p. 393). Springer Nature . Tom De Wolf, T. H. (2006). A Catalogue of Decentralised Coordination Mechanisms for Designing Self-Organising Emergent Applications. Department of Computer Science, K.U.Leuven. Vyacheslav Koryachko, D. P. (2017). Analysis of QoS Metrics in Software Defined. IEEE Xplore. Wendong, W., Qinglei, Q., Xiangyang, G., & Yanna, H. (2014). Autonomic QoS Management Mechanism in Software Defined Network. IEEE Xplore. Wu, J., Yunfeng, P., Meng , S., & Manman , C. (2019). Link Congestion Prediction using Machine Learning. IEEE Xplore. Zhang, Y. (2013). An adaptive flow counting method for anomaly detection. proc ACM CoNEXT, pp. 25–30. |
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Universidad Nacional de Colombia |
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Manizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automática |
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Facultad de Ingeniería y Arquitectura |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Toro García, Nicolás96d0ab564f7898356dfeff85bfc09ab7Aristizábal Quintero, Luz Angela2b9bcc6867756bddfa7fc34a7dcaf26f600Grupo de Investigación en Recursos Energéticos GireAristizábal Quintero, Luz Angela [0000-0003-4510-9029]Aristizábal Quintero, Luz Angela [0000219169]2023-02-13T14:42:53Z2023-02-13T14:42:53Z2022https://repositorio.unal.edu.co/handle/unal/83450Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/graficas, tablasEn esta tesis se estudian, adaptan y aplican dos prometedoras áreas de investigación influyentes en la automatización de Redes de Datos: las Redes definidas por software (Software Definid Network - SDN) y Procesamiento de señales en grafos (Graph Signal Processing -GSP). Este documento presenta un conjunto de estrategias, soportadas por la teoría de procesamiento de señales en grafos, para la autorregulación del comportamiento ante eventos de anormalidad en una red definida por software (SDN) y para la coordinación entre el conjunto de procesos que interactúan en la reprogramación de la Red Definida por Software para lograr un estado funcional acorde a parámetros de normalidad para la red. Las contribuciones y hallazgos más relevantes de esta tesis son los siguientes: 0) Se establece un puente referencial de aplicación entre las áreas de investigación SDN y GSP; 1) se propone e implementa un monitor multicapa, basado en la teoría de procesamiento de señales en grafos que explota las características inherentes a las Redes Definidas por Software (SDN); 2) se propone e implementa una estrategia de autorregulación del direccionamiento de tráfico en una SDN ante la presencia de anomalías, basado en la técnica de aprendizaje por refuerzo (Reinforcement Learning – RL) y en el procesamiento de señales en grafos (GSP); 3) Se propone e implementa una técnica de coordinación de las actividades inherentes a los procesos de adaptación sobre las SDN; 4) se presenta un esquema de conectividad de las herramientas de desarrollo de software que actualmente son utilizadas en procesos de automatización de redes de datos. (Texto tomado de la fuente)In this thesis, two promising research areas that are influential in the automation of Data Networks are studied, adapted and applied: Software Defined Networks (SDN) and Graph Signal Processing (GSP). This document presents a set of strategies, supported by the theory of signal processing in graphs, for the self-regulation of behavior in presence of abnormality events in a network and for the coordination between the set of processes that interact in the reprogramming of the Software Defined Network to achieve a functional state according to normality parameters for the network. The most relevant contributions and findings of this thesis are the following: 0) A referential application bridge is established between the SDN and GSP research areas 1) A multilayer monitor is proposed and implemented, based on the theory of signal processing in graphs that it exploits the characteristics inherent to Software Defined Networks (SDN); 2) a self-regulation strategy for traffic routing in an SDN in the presence of anomalies is proposed and implemented, based on the reinforcement learning technique (Reinforcement Learning - RL) and on graph signal processing (GSP). 3) A coordination technique for the activities inherent to the adaptation processes on SDN is proposed and implemented. 4) a connectivity scheme of the software development tools that are currently used in data network automation processes is presented.DoctoradoDoctor en IngenieríaAutoRedes de Datos y Redes IndustrialesEléctrica, Electrónica, Automatización Y Telecomunicaciones90 páginasapplication/pdfspaUniversidad Nacional de ColombiaManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - AutomáticaFacultad de Ingeniería y ArquitecturaManizales, ColombiaUniversidad Nacional de Colombia - Sede Manizales000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computaciónRedes definidas por softwareProcesamiento de señales gráficasAprendizaje por refuerzoAutorregulaciónSoftware defined networksGraphical signal processingSelf-regulated.Reinforcement learningAnálisis de redesNetwork analysisModelo de coordinación para la actividad autorregulada en redes definidas por softwareCoordination model for the self-regulated activity of software-defined networksTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06ImageTextACI. (s.f.). ACI policy Model.Recuperado de https://sandboxapicdc.cisco.com/help/content/index.html#c_about-cisco-aci.html.Adami, D. (2015). A Network Control Application enabling Software-defined Quality of Service. IEEE Xplore.Adrichem, N.V. Doerr, C.(2014) OpenNetMon: Network Monitoring in Openflow Software-Defined Networks. IEEE Network Operations and Management Symposium (NOMS), IEEE Xplore, 1-8.Akash, S., Varalakshmi, P., & Somasundaram, V. (2018). Congestion Control Mechanism in Software Defined Networking by Traffic Rerouting. IEEE Xplore.Ryan, G., & Tischer, J. (2017). Programming Automatic Cisco Networks. Cisco Press.ACI. (n.d.). ACI policy Model. https://sandboxapicdc.cisco.com/help/content/index.html#c_about-cisco-aci.html.Adami, D., & Donatini, L. (2015). A Network Control Application enabling Software-defined Quality of Service. IEEE Xplore.Astuto, B., Mendonca, M., & Nguy, X. (2014). A Survey of Software-Defined Networking: Past, Present. and Future of Programmable Networks. IEEE Communications Surveys and Tutorials 16 (3)., 1617 - 1634.Bailey, S., Balsal, D., & Dunbar, L. (2014). SDN Architecture Overview. Open Network Foundation. https://opennetworking.org/wp-content/uploads/2013/02/SDN-architecture-overview-1.0.pdf.Balasubramaniam, S., & Botvich, D. (2006). Applying Blood Glucose Homeostatic Model Towards Self-management of IP QoS Provisioned Networks. Autonomic Principles of IP Operations and Management. Springer, pp 84–95.Bocaletti, S., Bianconi, G., Criado, R., & Del Genio, C. (2014). The structure and dynamics of multilayer networks. Elsevier. Volume 544, Issue 1, 1-122.Ciancarini, P. (1996). Coordination models and languages as software integrators. ACM Computing Surveys, vol. 28, no. 2, 300–.CISCO. (2018). Indice de redes Visuales. https://newsroom.cisco.com/press-release-content?articleId=1955935.Hammond, D., Vandergheynst, P., & Gribonw, R. (2019). The Spectral Graph Wavelet Transform: Fundamental Theory and Fast Computation of Graph Signals. Springer International Publishing, 141-175.Hommond, D., Vandergheynst, P., & Gribonw, R. (2011). Wavelets on Graphs via Spectral Graph Theory. Applied and Computational Harmonic Analysis, Elsevier 30 (2), 129–150.Irion, J. (2015). Multiescale Transforms for signal on graph: Method and applications. University of California, Davis.James, R. K. (2017). Computer Networking: A Top-Down Approach . Pearson.Jammal, M., Singh, T., Shami, A., Asal, R., & Li, Y. (2014). Software defined networking: State of the art and research challenges. Computer Networks, vol. 72, 74-98.Kathiravelu, P. (2016). Software-Defined Networking-Based Enhancements to Data Quality and QoS in Multi-Tenanted Data Center Clouds. IEEE Xplore.Kobayashi, M., Seetharaman, S., & Parulka, G. (2014). Maturing of OpenFlow and Software-defined Networking through deployments. Computer Networks. 61, 151–175.Kurose, J. F. (2020). Computer Networking . Pearson.Lara, A., & Kolasani , A. (2014). Network Innovation using OpenFlow: A Survey. IEEE Communications Surveys & Tutorials, Vol. 16, No. 3.Latif, Z., Sharif, K., Li, F., & Monjurul, K. (2020). A comprehensive survey of interface protocols for software defined networks. Journal of Network and Computer Applications.Laurençon, H., Ségerie, C.-R., & Lu, J. (2021). Continuous Homeostatic Reinforcement Learning for Self-Regulated Autonomous Agents. Cornell University, https://arxiv.org/abs/2109.06580.Luong, D., Outtagarts, A., & Hebbar, A. (2016). Traffic Monitoring in Software Defined Netwoks Using Opendaylight Controller. Lecture Notes in Computer Science, LNCS, Vol. 10026, Springer., pp 38-48.Ma , J., Huang, W., & Segarra, S. (2016). Difusión Filtering of Graph Signal and its Use in Recomendation Systems. IEEExplore.Manar, J., Taranpreet, S., & Abdallah, S. (2014). Software defined networking: State of the art and research challenges. Computer Network.Martolia, K. A. (2016). Congestion Control Techniques. International Conference on Communication and Signal Processing (ICCSP). IEEE Xplore.Mckeown, N., Balakrishnan , G., Parulkar, L., & Peterson, J. (2008). Openflow: Enabling innovation in campus networks. SIGCOMM CCR, Vol. 38, Nro. 2., 69-74.Mizuki, O., & Ikegami, A. (2014). Dynamic homeostasis in packet switching networks. International. Society for Adaptive Behavior.N.L.M. van Adrichem, C. D., van Adriechem , N., Doerr, C., & Kuiper, F. (2014). OpenNetMon: Network Monitoring in Openflow Software-Defined Networks. IEEE Network Operations and Management Symposium (NOMS), IEEE Xplore, 1-8.Omicini, A. (2013). Nature-Inspired Coordination Models: Current Status and future trends. ISRN Software Engineering.ON-LINE. (2014). Open Flow. https://www.opennetworking.org/wp-content/uploads/2014/10/openflow-switch-v1.5.1.pdf.openflex. (n.d.). OpenFlex vs Openflow. https://www.cisco.com/c/en/us/solutions/collateral/data-center-virtualization/application-centric-infrastructure/white-paper-c11-731302.html.Pérez, D. M. (2018). Administración y seguridad en redes de computadoras. . Alfa-Omega .Perumal, S., & Varalakshmi, S. (2017). Research Confront in Software Defined Networking Environment: A Survey. Springer Communications in Computer and Information Science.Przemysław Ignaciuk, A. B. (2013). Congestion Control in Data Transmission Networks. Springer.Qiang, H., Min, H., Xiangyi, C., & Wang, X. (2020). Prediction for Energy Efficiency Optimization in Software-Defined Networking. IEEE Xplore.Ren, C. W., & Yongcan. (2011). Distributed Coordination of multiagent networks. Springer-Verlag.Sandryhaila, A., & Moura, J. (2013). Discrete Signal Processing on Graphs. IEEE TRANSACTIONS ON SIGNAL PROCESSING.SevOne. (n.d.). Software Defined Network Monitoring. https://www.sevone.com/wp-content/uploads/2021/04/SDN_Solutions_Guide-1.pdf.Shumman, D., Narang, S., Frossard, P., & Ortega, A. (2013). The Emerging Field of Signal Processing On Graphs: Extending High- Dimensional Data Analysis to Networks and Other Irregular Domains. IEEE Signal processing Magazine, 83 – 98.Sifakis, J. (2019). Autonomous Systems – An Architectural. In F. C. Michele Loreti, Models, Languages, and Tools (p. 393). Springer Nature .Tom De Wolf, T. H. (2006). A Catalogue of Decentralised Coordination Mechanisms for Designing Self-Organising Emergent Applications. Department of Computer Science, K.U.Leuven.Vyacheslav Koryachko, D. P. (2017). Analysis of QoS Metrics in Software Defined. IEEE Xplore.Wendong, W., Qinglei, Q., Xiangyang, G., & Yanna, H. (2014). Autonomic QoS Management Mechanism in Software Defined Network. IEEE Xplore.Wu, J., Yunfeng, P., Meng , S., & Manman , C. (2019). Link Congestion Prediction using Machine Learning. IEEE Xplore.Zhang, Y. (2013). An adaptive flow counting method for anomaly detection. proc ACM CoNEXT, pp. 25–30.Modelo Ecosistémico de Mejoramiento Rural y Construcción de Paz: Instalación de Capacidades LocalesFinanciado en el marco de la convocatoria Colombia CientíficaBibliotecariosEstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83450/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL24625353.2022.pdf24625353.2022.pdfTesis de Doctorado en Ingeniería - Automáticaapplication/pdf2364045https://repositorio.unal.edu.co/bitstream/unal/83450/2/24625353.2022.pdf374d128c8139cd871472f668503517d6MD52THUMBNAIL24625353.2022.pdf.jpg24625353.2022.pdf.jpgGenerated Thumbnailimage/jpeg4008https://repositorio.unal.edu.co/bitstream/unal/83450/3/24625353.2022.pdf.jpg30e6de5e0c442297dd6a8dbde97aad74MD53unal/83450oai:repositorio.unal.edu.co:unal/834502023-08-16 23:03:47.862Repositorio Institucional Universidad Nacional de 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