Video analytics strategies at the edge of network
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/31295 |
Resumo: | Several technologies are being presented with 5G networks’ evolution, technologies such as Network Functions Virtualization (NFV) and Software Defined Networking (SDN). With this evolution, we are getting closer and closer to the appearance of Smart Cities. Smart Cities will enjoy enough of the evolution of this new type of network. Cities like these are characterized by high numbers of users and a high number of equipment connected to the network (sensors, cameras). All these decrease the quality of service, while latency increases, giving a bad experience for the users. The triggering factor for this increased latency and decreased quality of service could be the existing video processing. With all the video data crossing the network, it makes the connections busier, thus worsening the other users’ quality of service. It is here where this Thesis comes in, placing this processing closer to the equipment on Edge Datacenters so that this data does not occupy the central ones. Using SDN for traffic control and Video analytics applications to process data sent to these locations. A study was carried out to study the impact of this SDN technology on a network characterized by video traffic. The results show that SDN does not impose any negative impact on this network, nor an added computational weight, and we concluded that performance had not been lost compared to traditional networks. We do gain network flexibility and programmability compared to traditional networks. |
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Video analytics strategies at the edge of networkSDN5G networksEdge datacenterVideoAnalyticsQoSSeveral technologies are being presented with 5G networks’ evolution, technologies such as Network Functions Virtualization (NFV) and Software Defined Networking (SDN). With this evolution, we are getting closer and closer to the appearance of Smart Cities. Smart Cities will enjoy enough of the evolution of this new type of network. Cities like these are characterized by high numbers of users and a high number of equipment connected to the network (sensors, cameras). All these decrease the quality of service, while latency increases, giving a bad experience for the users. The triggering factor for this increased latency and decreased quality of service could be the existing video processing. With all the video data crossing the network, it makes the connections busier, thus worsening the other users’ quality of service. It is here where this Thesis comes in, placing this processing closer to the equipment on Edge Datacenters so that this data does not occupy the central ones. Using SDN for traffic control and Video analytics applications to process data sent to these locations. A study was carried out to study the impact of this SDN technology on a network characterized by video traffic. The results show that SDN does not impose any negative impact on this network, nor an added computational weight, and we concluded that performance had not been lost compared to traditional networks. We do gain network flexibility and programmability compared to traditional networks.Com a evolução das redes 5G, várias tecnologias vão sendo apresentadas tais como Funções de Redes Virtualizadas (NFV) e Redes Definidas por Software (SDN). Com esta evolução ficamos cada vez mais perto do aparecimento de Cidades Inteligentes, estas iriam usufruir bastante da evolução deste novo tipo de rede. Cidades como estas são caracterizadas por elevados números de utilizadores assim como elevado número de equipamentos (sensores, câmeras) com ligação à rede. Tudo isto provoca, por vezes, que a qualidade de serviço diminua e a latência aumenta dando uma má experiência para os utilizadores. O fator provocante deste aumento de latência e diminuição de qualidade de serviço poderia ser o processamento de vídeo existente nos datacenters centrais. Como todos os dados de vídeo a atravessarem a rede torna as suas ligações mais ocupadas, piorando assim a qualidade para os restantes. Sendo aqui que entra esta dissertação, colocar este processamento mais perto dos equipamentos, nos Edge Datacenters de modo a que estes dados não ocupem os nós centrais. Fazendo uso de SDN para controlo de tráfego e aplicações de Vídeo Analytics para o processamento dos dados encaminhados para estes locais. Foi realizado então o estudo do impacto desta tecnologia SDN numa rede caracterizada por ter tráfego de vídeo. Os resultados demonstram que SDN não impõe impacto nesta rede, nem um peso computacional acrescentado, ou seja não se perdeu performance comparativamente às redes tradicionais. Ganhando sim flexibilidade e a programabilidade da rede comparativamente às redes tradicionais.2023-02-23T00:00:00Z2021-02-18T00:00:00Z2021-02-18info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/31295engMendes, Bruno Miguel Fonsecainfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T12:00:24Zoai:ria.ua.pt:10773/31295Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:03:12.491153Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Video analytics strategies at the edge of network |
title |
Video analytics strategies at the edge of network |
spellingShingle |
Video analytics strategies at the edge of network Mendes, Bruno Miguel Fonseca SDN 5G networks Edge datacenter Video Analytics QoS |
title_short |
Video analytics strategies at the edge of network |
title_full |
Video analytics strategies at the edge of network |
title_fullStr |
Video analytics strategies at the edge of network |
title_full_unstemmed |
Video analytics strategies at the edge of network |
title_sort |
Video analytics strategies at the edge of network |
author |
Mendes, Bruno Miguel Fonseca |
author_facet |
Mendes, Bruno Miguel Fonseca |
author_role |
author |
dc.contributor.author.fl_str_mv |
Mendes, Bruno Miguel Fonseca |
dc.subject.por.fl_str_mv |
SDN 5G networks Edge datacenter Video Analytics QoS |
topic |
SDN 5G networks Edge datacenter Video Analytics QoS |
description |
Several technologies are being presented with 5G networks’ evolution, technologies such as Network Functions Virtualization (NFV) and Software Defined Networking (SDN). With this evolution, we are getting closer and closer to the appearance of Smart Cities. Smart Cities will enjoy enough of the evolution of this new type of network. Cities like these are characterized by high numbers of users and a high number of equipment connected to the network (sensors, cameras). All these decrease the quality of service, while latency increases, giving a bad experience for the users. The triggering factor for this increased latency and decreased quality of service could be the existing video processing. With all the video data crossing the network, it makes the connections busier, thus worsening the other users’ quality of service. It is here where this Thesis comes in, placing this processing closer to the equipment on Edge Datacenters so that this data does not occupy the central ones. Using SDN for traffic control and Video analytics applications to process data sent to these locations. A study was carried out to study the impact of this SDN technology on a network characterized by video traffic. The results show that SDN does not impose any negative impact on this network, nor an added computational weight, and we concluded that performance had not been lost compared to traditional networks. We do gain network flexibility and programmability compared to traditional networks. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-02-18T00:00:00Z 2021-02-18 2023-02-23T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/31295 |
url |
http://hdl.handle.net/10773/31295 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/embargoedAccess |
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embargoedAccess |
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application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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