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Please use this identifier to cite or link to this item: https://tede.ufam.edu.br/handle/tede/7697
Tipo do documento: Tese
Título: DeepBGP: A Machine Learning Solution to reduce BGP Routing Convergence Time by Fine-Tuning MRAI
Título(s) alternativo(s): A Machine-Learning Solution to reduce BGP Routing Convergence Time in a Hybrid SDN-Interdomain environment by Fine-Tuning MRAI
Autor: Silva, Ricardo Bennesby da 
Primeiro orientador: Mota, Edjard Souza
Primeiro membro da banca: Feitosa, Eduardo Luzeiro
Segundo membro da banca: Santos, Eulanda Miranda dos
Terceiro membro da banca: Souza, Jose Neuman de
Quarto membro da banca: Cunha, Italo Fernando Scotá
Resumo: The organization of the Internet is composed of administrative domains, known as Autonomous Systems (ASes), that exchange reachability information by means of the Border Gateway Protocol (BGP). Since a high convergence delay leads to packet losses and service unavailability, such a protocol has to converge as fast as possible. As this can happen due to BGP's own mechanism of UPDATE messages, that produces a humongous amount of messages, BGP reduces the number of UPDATEs exchanged between two BGP routers by holding consecutive announcements from a router to a neighbor for a given amount of time. The BGP timer responsible for this task is called Minimum Route Advertisement Interval (MRAI), which has an important impact in routing convergence. The Software-Defined Networking (SDN) paradigm can be used to leverage interdomain routing services performance via the logically centralized controlling benefits of intradomain settings. SDN principles has been successfully deployed in data centers, LANs, and in several other studies, where each each AS is modeled with a logically centralized routing control, offering new opportunities and bringing BGP routing convergence improvements. In this work, an extensive survey is presented on the state-of-the-art about research efforts to achieve better BGP routing convergence time. Furthermore, I pinpoint the open issues in this research field and propose DeepBGP, to the best of my knowledge, the first hybrid framework endowed with a learning mechanism, that integrates the SDN paradigm within interdomain routing domains, to improve the interdomain routing convergence time. This is achieved by employing the LSTM learning technique that allows the tuning of MRAI value aiming to reduce the convergence time according to learned patterns from collected BGP UPDATE features. The PEERING platform was used to provide a real scenario that allows the sending of announcements to the Internet. With the benefits of having such an actual testbed I carried out experiments with protocol characteristics that can impact the routing convergence. The experimental results show that the adaptive MRAI in the DeepBGP framework is able to reduce the BGP routing convergence time when compared to the use of static MRAIs.
Abstract: The organization of the Internet is composed of administrative domains, known as Autonomous Systems (ASes), that exchange reachability information by means of the Border Gateway Protocol (BGP). Since a high convergence delay leads to packet losses and service unavailability, such a protocol has to converge as fast as possible. As this can happen due to BGP's own mechanism of UPDATE messages, that produces a humongous amount of messages, BGP reduces the number of UPDATEs exchanged between two BGP routers by holding consecutive announcements from a router to a neighbor for a given amount of time. The BGP timer responsible for this task is called Minimum Route Advertisement Interval (MRAI), which has an important impact in routing convergence. The Software-Defined Networking (SDN) paradigm can be used to leverage interdomain routing services performance via the logically centralized controlling benefits of intradomain settings. SDN principles has been successfully deployed in data centers, LANs, and in several other studies, where each each AS is modeled with a logically centralized routing control, offering new opportunities and bringing BGP routing convergence improvements. In this work, an extensive survey is presented on the state-of-the-art about research efforts to achieve better BGP routing convergence time. Furthermore, I pinpoint the open issues in this research field and propose DeepBGP, to the best of my knowledge, the first hybrid framework endowed with a learning mechanism, that integrates the SDN paradigm within interdomain routing domains, to improve the interdomain routing convergence time. This is achieved by employing the LSTM learning technique that allows the tuning of MRAI value aiming to reduce the convergence time according to learned patterns from collected BGP UPDATE features. The PEERING platform was used to provide a real scenario that allows the sending of announcements to the Internet. With the benefits of having such an actual testbed I carried out experiments with protocol characteristics that can impact the routing convergence. The experimental results show that the adaptive MRAI in the DeepBGP framework is able to reduce the BGP routing convergence time when compared to the use of static MRAIs.
Palavras-chave: Gerenciamento de redes
Roteamento entre domínios
Tempo de convergência
Border Gateway Protocol
Long Short-Term Memory
Long Short-Term Memory
Área(s) do CNPq: CIÊNCIAS EXATAS E DA TERRA: CIÊNCIA DA COMPUTAÇÃO: SISTEMAS DE COMPUTAÇÃO
???metadata.dc.subject.user???: bgp
convergence time
lstm
network management
interdomain routing
Idioma: eng
País: Brasil
Instituição: Universidade Federal do Amazonas
Sigla da instituição: UFAM
Departamento: Instituto de Computação
Programa: Programa de Pós-graduação em Informática
Citação: SILVA, Ricardo Bennesby da. DeepBGP: a machine learning solution to reduce BGP routing convergence time by Fine-Tuning MRAI. 2019. 141 f. Tese (Doutorado em Informática) - Universidade Federal do Amazonas, Manaus, 2019.
Tipo de acesso: Acesso Aberto
URI: https://tede.ufam.edu.br/handle/tede/7697
Data de defesa: 18-Nov-2019
Appears in Collections:Doutorado em Informática

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