Orquestração Dinâmica de Encadeamento de Funções de Serviço para Realidade Aumentada Multiusuário
Resumo
A Realidade Aumentada Multiusuário (RAMU) usa computação em borda para colaboração em ambiente 3D em dispositivos móveis. Esse serviço pode ser decomposto em Service Function Chainings (SFCs) distribuídas em servidores de borda, permitindo execução paralela de usuários. Propomos a Orquestração de Encadeamento de Funções de Serviço Multicritério e Sensível à Mobilidade (OSFEM), que reduz latência e otimiza recursos de rede. Os resultados mostram que a OSFEM melhora a aceitação de sessões, a eficiência de recursos e a diminuição de latência em comparação com métodos existentes.Referências
Akhtar et al. (2021). Managing chains of application functions over multi-technology edge networks. IEEE Trans. on Network and Service Management.
Andrew, S., Bos, H., et al. (2024). Sistemas operacionais modernos. Bookman Editora.
Huang et al (2021). Proactive edge cloud optimization for mobile augmented reality applications. In IEEE Wireless Communications and Networking Conference (WCNC). IEEE.
L. Wang et al. (2021). Change: Delay-aware service function chain orchestration at the edge. In IEEE International Conference on Fog and Edge Computing (ICFEC). IEEE.
Lin, I.-C., Yeh, Y.-H., and Lin, K. C.-J. (2021). Toward optimal partial parallelization for service function chaining. IEEE/ACM Transactions on Networking, 29(5):2033–2044.
Liu et al. (2018). An edge network orchestrator for mobile augmented reality. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE.
Medeiros, A., Di Maio, A., Braun, T., and Neto, A. (2022). Service chaining graph: Latency-and energy-aware mobile vr deployment over mec infrastructures. In GLOBECOM 2022-2022 IEEE Global Communications Conference, pages 6133–6138. IEEE.
Ngo, M. et al. (2020). Coordinated container migration and base station handover in mobile edge computing. In IEEE Global Communications Conference, pages 1–6.
Perronnin et al. (2010). Large-scale image retrieval with compressed fisher vectors. In IEEE computer society conference on computer vision and pattern recognition. IEEE.
Santos, H., Martins, B., Rosário, D., Cerqueira, E., and Braun, T. (2023). Mobility-aware service function chaining orchestration for multi-user augmented reality. In 2023 IEEE 48th Conference on Local Computer Networks (LCN), pages 1–9. IEEE.
Santos, H., Rosario, D., Cerqueira, E., and Braun, T. (2022). Multi-criteria service function chaining orchestration for multi-user virtual reality services. In GLOBECOM 2022-2022 IEEE Global Communications Conference, pages 6360–6365. IEEE.
Santos, J. et al. (2021). Efficient orchestration of service chains in fog computing for immersive media. In 17th International Conference on Network and Service Management (CNSM), pages 139–145. IEEE.
T. Wang et al. (2020). Adaptive service function chain scheduling in mobile edge computing via deep reinforcement learning. IEEE Access.
Andrew, S., Bos, H., et al. (2024). Sistemas operacionais modernos. Bookman Editora.
Huang et al (2021). Proactive edge cloud optimization for mobile augmented reality applications. In IEEE Wireless Communications and Networking Conference (WCNC). IEEE.
L. Wang et al. (2021). Change: Delay-aware service function chain orchestration at the edge. In IEEE International Conference on Fog and Edge Computing (ICFEC). IEEE.
Lin, I.-C., Yeh, Y.-H., and Lin, K. C.-J. (2021). Toward optimal partial parallelization for service function chaining. IEEE/ACM Transactions on Networking, 29(5):2033–2044.
Liu et al. (2018). An edge network orchestrator for mobile augmented reality. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE.
Medeiros, A., Di Maio, A., Braun, T., and Neto, A. (2022). Service chaining graph: Latency-and energy-aware mobile vr deployment over mec infrastructures. In GLOBECOM 2022-2022 IEEE Global Communications Conference, pages 6133–6138. IEEE.
Ngo, M. et al. (2020). Coordinated container migration and base station handover in mobile edge computing. In IEEE Global Communications Conference, pages 1–6.
Perronnin et al. (2010). Large-scale image retrieval with compressed fisher vectors. In IEEE computer society conference on computer vision and pattern recognition. IEEE.
Santos, H., Martins, B., Rosário, D., Cerqueira, E., and Braun, T. (2023). Mobility-aware service function chaining orchestration for multi-user augmented reality. In 2023 IEEE 48th Conference on Local Computer Networks (LCN), pages 1–9. IEEE.
Santos, H., Rosario, D., Cerqueira, E., and Braun, T. (2022). Multi-criteria service function chaining orchestration for multi-user virtual reality services. In GLOBECOM 2022-2022 IEEE Global Communications Conference, pages 6360–6365. IEEE.
Santos, J. et al. (2021). Efficient orchestration of service chains in fog computing for immersive media. In 17th International Conference on Network and Service Management (CNSM), pages 139–145. IEEE.
T. Wang et al. (2020). Adaptive service function chain scheduling in mobile edge computing via deep reinforcement learning. IEEE Access.
Publicado
20/05/2024
Como Citar
FLEXA, Rodrigo; SANTOS, Hugo; CERQUEIRA, Eduardo; ROSÁRIO, Denis.
Orquestração Dinâmica de Encadeamento de Funções de Serviço para Realidade Aumentada Multiusuário. In: WORKSHOP DE TRABALHOS DE INICIAÇÃO CIENTÍFICA E DE GRADUAÇÃO - SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 42. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 233-240.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2024.3388.
