Dynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networks

Detalhes bibliográficos
Autor(a) principal: SOUZA, Caio Bruno Bezerra de
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Institucional da UFPE
Texto Completo: https://repositorio.ufpe.br/handle/123456789/54702
Resumo: The Fifth Generation of mobile networks (5G) seeks to support a diversity of applications categorized into three types: enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and Ultra Reliable Low Latency Communications (URLLC), being their coexistence a major challenge. Multi-access Edge Computing (MEC), Network Function Virtualization (NFV) and Network Slicing (NS) emerge as complementary paradigms that shall support both eMBB and URLLC by offering fine-grained on-demand distributed resources closer to the User Equipment (UE) with a shared utilization of physical infrastructure. In this work, we have addressed the combination of MEC, NFV, NS and dynamic virtual resource allocation in order to overcome the problem of resource dimensioning in the network edge core. Thus, we have designed an analytical model to evaluate how requests are managed by the virtualization resources of a single MEC node, with a primary focus on meeting the requirements of both eMBB and URLLC services. We proposed a CTMC-based model to characterize dynamic virtual resource allocation and incorporated five performance metrics, which are relevant not only for URLLC and eMBB services (e.g., availability and response time) but also for service providers (e.g., power consumption), integrating practical factors like resource failures, service prioritization, and setup (repair) times into the formulation. This model enables an understanding of how the 5G network core behaves in serving different service categories by applying service prioritization to efficiently share processing resources. Some of our key findings include the idea that higher eMBB arrival rates decrease availability and increase response times up to 300 ms, while URLLC availability remains stable. Moreover, the container setup rates and failure rates substantially affect both availability and response times, with higher setup rates enhancing availability by up to 30% and reducing response times by 60%. Also, the number of containers emerges as a significant factor, enhancing both availability and response times, while buffer sizes mainly impact response times. In brief, our work advances in the current state of the art of the MEC-NFV domain by providing valuable insights for the design of MEC-NFV architecture, business models, and mechanisms to address communication constraints.
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spelling SOUZA, Caio Bruno Bezerra dehttp://lattes.cnpq.br/5915479506163386http://lattes.cnpq.br/9825617657358787http://lattes.cnpq.br/0796298494476092BALIEIRO, Andson MarreirosFALCÃO, Marcos Rocha de Moraes2024-01-23T16:16:57Z2024-01-23T16:16:57Z2023-09-29SOUZA, Caio Bruno Bezerra de. Dynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networks. 2023. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de Pernambuco, Recife, 2023.https://repositorio.ufpe.br/handle/123456789/54702The Fifth Generation of mobile networks (5G) seeks to support a diversity of applications categorized into three types: enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and Ultra Reliable Low Latency Communications (URLLC), being their coexistence a major challenge. Multi-access Edge Computing (MEC), Network Function Virtualization (NFV) and Network Slicing (NS) emerge as complementary paradigms that shall support both eMBB and URLLC by offering fine-grained on-demand distributed resources closer to the User Equipment (UE) with a shared utilization of physical infrastructure. In this work, we have addressed the combination of MEC, NFV, NS and dynamic virtual resource allocation in order to overcome the problem of resource dimensioning in the network edge core. Thus, we have designed an analytical model to evaluate how requests are managed by the virtualization resources of a single MEC node, with a primary focus on meeting the requirements of both eMBB and URLLC services. We proposed a CTMC-based model to characterize dynamic virtual resource allocation and incorporated five performance metrics, which are relevant not only for URLLC and eMBB services (e.g., availability and response time) but also for service providers (e.g., power consumption), integrating practical factors like resource failures, service prioritization, and setup (repair) times into the formulation. This model enables an understanding of how the 5G network core behaves in serving different service categories by applying service prioritization to efficiently share processing resources. Some of our key findings include the idea that higher eMBB arrival rates decrease availability and increase response times up to 300 ms, while URLLC availability remains stable. Moreover, the container setup rates and failure rates substantially affect both availability and response times, with higher setup rates enhancing availability by up to 30% and reducing response times by 60%. Also, the number of containers emerges as a significant factor, enhancing both availability and response times, while buffer sizes mainly impact response times. In brief, our work advances in the current state of the art of the MEC-NFV domain by providing valuable insights for the design of MEC-NFV architecture, business models, and mechanisms to address communication constraints.A Quinta Geração de redes móveis (5G) busca suportar diversas aplicações categorizadas em três tipos: largura de banda móvel melhorada (eMBB), comunicação do tipo máquina mas- siva (mMTC) e comunicação com baixa latência e confiabilidade muito alta (URLLC), em que a coexistência delas é um grande desafio. A computação de borda multiacesso (MEC), virtualização de funções de rede (NFV) e o fatiamento de rede (NS) surgem como paradig- mas complementares para assistir tanto serviços eMBB quanto URLLC, oferecendo recursos distribuídos sob demanda e de maneira otimizada, mais próximos do equipamento do usuário (UE), com utilização compartilhada da infraestrutura física. Este trabalho explora a integração de MEC, NFV, NS e alocação dinâmica de recursos virtuais para endereçar o problema de di- mensionamento na rede de borda. Para isso, utiliza-se um modelo analítico para avaliar como as solicitações são gerenciadas pelos recursos de virtualização em um único nó MEC, com ên- fase nos requisitos dos serviços eMBB e URLLC. Um modelo baseado em CTMC foi proposto para caracterizar a alocação dinâmica de recursos virtuais e a derivaçao de cinco métricas de desempenho é realizada, as quais são relevantes não apenas para serviços URLLC e eMBB (e.g., disponibilidade e tempo de resposta), mas também para provedores de serviços (e.g., consumo de energia). Além disso, o modelo integra fatores práticos como falhas nos recursos, priorização de serviços e tempos de configuração e reparo na formulação. Desta forma, o mod- elo permite compreender como o núcleo da rede 5G se comporta no atendimento a diferentes categorias de serviços, aplicando a priorização de serviços para compartilhar eficientemente os recursos de processamento. Algumas descobertas incluem a ideia de que taxas mais altas de chegada eMBB diminuem a disponibilidade e aumentam os tempos de resposta para até 300 ms, enquanto a disponibilidade para URLLC permanece estável. Além disso, as taxas de configuração de contêineres e as taxas de falhas afetam substancialmente a disponibilidade e os tempos de resposta, com taxas de configuração mais altas aumentando a disponibilidade em até 30% e reduzindo os tempos de resposta em 60%. Ademais, o número de contentores surge como um fator significativo, melhorando tanto a disponibilidade como os tempos de resposta, enquanto os tamanhos dos buffers afetam principalmente os tempos de resposta. Em resumo, nosso trabalho avança no estado da arte atual do domínio MEC-NFV, fornecendo insights valiosos para o dimensionamento da arquitetura MEC-NFV, modelos de negócios e mecanismos para lidar com alocação de recursos sob diferentes restrições de comunicação.engUniversidade Federal de PernambucoPrograma de Pos Graduacao em Ciencia da ComputacaoUFPEBrasilAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccess5GURLLCeMBBMECNFVAlocação de recursosDynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networksinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesismestradoreponame:Repositório Institucional da UFPEinstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPECC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.ufpe.br/bitstream/123456789/54702/2/license_rdfe39d27027a6cc9cb039ad269a5db8e34MD52LICENSElicense.txtlicense.txttext/plain; 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dc.title.pt_BR.fl_str_mv Dynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networks
title Dynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networks
spellingShingle Dynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networks
SOUZA, Caio Bruno Bezerra de
5G
URLLC
eMBB
MEC
NFV
Alocação de recursos
title_short Dynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networks
title_full Dynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networks
title_fullStr Dynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networks
title_full_unstemmed Dynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networks
title_sort Dynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networks
author SOUZA, Caio Bruno Bezerra de
author_facet SOUZA, Caio Bruno Bezerra de
author_role author
dc.contributor.authorLattes.pt_BR.fl_str_mv http://lattes.cnpq.br/5915479506163386
dc.contributor.advisorLattes.pt_BR.fl_str_mv http://lattes.cnpq.br/9825617657358787
dc.contributor.advisor-coLattes.pt_BR.fl_str_mv http://lattes.cnpq.br/0796298494476092
dc.contributor.author.fl_str_mv SOUZA, Caio Bruno Bezerra de
dc.contributor.advisor1.fl_str_mv BALIEIRO, Andson Marreiros
dc.contributor.advisor-co1.fl_str_mv FALCÃO, Marcos Rocha de Moraes
contributor_str_mv BALIEIRO, Andson Marreiros
FALCÃO, Marcos Rocha de Moraes
dc.subject.por.fl_str_mv 5G
URLLC
eMBB
MEC
NFV
Alocação de recursos
topic 5G
URLLC
eMBB
MEC
NFV
Alocação de recursos
description The Fifth Generation of mobile networks (5G) seeks to support a diversity of applications categorized into three types: enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and Ultra Reliable Low Latency Communications (URLLC), being their coexistence a major challenge. Multi-access Edge Computing (MEC), Network Function Virtualization (NFV) and Network Slicing (NS) emerge as complementary paradigms that shall support both eMBB and URLLC by offering fine-grained on-demand distributed resources closer to the User Equipment (UE) with a shared utilization of physical infrastructure. In this work, we have addressed the combination of MEC, NFV, NS and dynamic virtual resource allocation in order to overcome the problem of resource dimensioning in the network edge core. Thus, we have designed an analytical model to evaluate how requests are managed by the virtualization resources of a single MEC node, with a primary focus on meeting the requirements of both eMBB and URLLC services. We proposed a CTMC-based model to characterize dynamic virtual resource allocation and incorporated five performance metrics, which are relevant not only for URLLC and eMBB services (e.g., availability and response time) but also for service providers (e.g., power consumption), integrating practical factors like resource failures, service prioritization, and setup (repair) times into the formulation. This model enables an understanding of how the 5G network core behaves in serving different service categories by applying service prioritization to efficiently share processing resources. Some of our key findings include the idea that higher eMBB arrival rates decrease availability and increase response times up to 300 ms, while URLLC availability remains stable. Moreover, the container setup rates and failure rates substantially affect both availability and response times, with higher setup rates enhancing availability by up to 30% and reducing response times by 60%. Also, the number of containers emerges as a significant factor, enhancing both availability and response times, while buffer sizes mainly impact response times. In brief, our work advances in the current state of the art of the MEC-NFV domain by providing valuable insights for the design of MEC-NFV architecture, business models, and mechanisms to address communication constraints.
publishDate 2023
dc.date.issued.fl_str_mv 2023-09-29
dc.date.accessioned.fl_str_mv 2024-01-23T16:16:57Z
dc.date.available.fl_str_mv 2024-01-23T16:16:57Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
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dc.identifier.citation.fl_str_mv SOUZA, Caio Bruno Bezerra de. Dynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networks. 2023. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de Pernambuco, Recife, 2023.
dc.identifier.uri.fl_str_mv https://repositorio.ufpe.br/handle/123456789/54702
identifier_str_mv SOUZA, Caio Bruno Bezerra de. Dynamic resource allocation for URLLC and eMBB services in NFV-MEC 5G networks. 2023. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de Pernambuco, Recife, 2023.
url https://repositorio.ufpe.br/handle/123456789/54702
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dc.publisher.none.fl_str_mv Universidade Federal de Pernambuco
dc.publisher.program.fl_str_mv Programa de Pos Graduacao em Ciencia da Computacao
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