Distribuição de Conteúdo Sob Demanda Através da Alocação de Microserviços Dinâmicos na Borda e Núcleo da Rede
Resumo
A distribuição de conteúdo de vídeo sob Demanda (VoD) está se popularizando e será o principal tipo de aplicação utilizada na Internet. Para acomodar o tráfego de VoD e prover disseminação com Qualidade de Experiência (QoE), microserviços surgem como o modelo ideal para explorar a implantação de serviços de cache em diferentes níveis de uma arquitetura de computação em névoa. A utilização de microserviços na distribuição de conteúdo de vídeo é estratégica, pois permite alocar os recursos em nós da névoa de forma dinâmica e customizada com a demanda dos clientes. Este artigo apresenta Fog4MS, um mecanismo para alocação dinâmica de microserviços para cache de VoD em ambientes de computação em névoa. O mecanismo Fog4MS considera o atraso, tempo de migração do conteúdo e taxa de utilização do nó da névoa para calcular a melhor localização das caches considerando a arquitetura de computação em névoa multiníveis. Experimentos realizados em simulação mostram a eficiência da proposta comparada a mecanismos existentes quando considerando custo, tempo de migração do conteúdo, índice de justiça e QoE.
Palavras-chave:
Video sob demanda, Computação em Névoa, Microserviços
Referências
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Aazam, M. and Huh, E.-N. (2015). Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In 29th International Conference on Advanced Information Networking and Applications, pages 687–694.
Araújo, F., Rosário, D., Cerqueira, E., and Villas, L. A. (2019). A hybrid energy-aware video bitrate adaptation algorithm for mobile networks. In 15th Annual Conference on Wireless On-demand Network Systems and Services (WONS), pages 146–153. IEEE.
Bhardwaj, K. et al. (2019). Addressing the Fragmentation Problem in Distributed and Decentralized Edge Computing: A Vision. In IEEE International Conference on Cloud Engineering (IC2E), pages 156–167.
Chunlin, L. et al. (2019). Optimal media service selection scheme for mobile users in mobile cloud. Wireless Networks, 25(6):3179–3192.
Cisco (2019). Cisco visual networking index: Forecast and trends, 2017–2022. Technical report, Cisco.
He, Q. et al. (2017). Fog-based transcoding for crowdsourced video livecast. IEEE Communications Magazine, 55(4):28–33.
Jain, R. K., Chiu, D.-M. W., and Hawe, W. R. (1984). A quantitative measure of fairness and discrimination. Eastern Research Laboratory, Digital Equipment Corporation, Hudson, MA.
Jalali, F. et al. (2016). Fog computing may help to save energy in cloud computing. IEEE Journal on Selected Areas in Communications, 34(5):1728–1739.
Juluri, P., Tamarapalli, V., and Medhi, D. (2016). Measurement of Quality of Experience of Video-on-Demand Services: A Survey. IEEE Communications Surveys & Tutorials, 18(1):401–418.
Mao, Y. et al. (2017). A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Communications Surveys Tutorials, 19(4):2322–2358.
Masip-Bruin, X. et al. (2016). Foggy Clouds and Cloudy Fogs: a Real Need for Coordinated Management of Fog-to-Cloud Computing Systems. IEEE Wireless Communications, 23(5):120–128.
Menchaca-Mendez, R. et al. (2018). Opportunistic Mobile Sensing in the Fog. Wireless Communications and Mobile Computing, 2018:1–18.
Ni, L. et al. (2017). Resource allocation strategy in fog computing based on priced timed petrinets. IEEE Internet of Things Journal, 4(5):1216–1228.
Padhye, J. et al. (2000). Modeling tcp reno performance: a simple model and its empirical validation. IEEE/ACM transactions on Networking, 8(2):133–145.
Riley, G. F. and Henderson, T. R. (2010). The ns-3 network simulator. In Wehrle, K., Günes, M., and Gross, J., editors, Modeling and Tools for Network Simulation, pages 15–34. Springer.
Rosário, D. et al. (2018). Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support. Sensors, 18(2).
Salahuddin, M. A. et al. (2018). A survey on content placement algorithms for cloud-based content delivery networks. IEEE Access, 6:91–114.
Siavoshani, M. J., Pourmiri, A., and Shariatpanahi, S. P. (2017). Storage, communication, and load balancing trade-off in distributed cache networks. IEEE Transactions on Parallel and Distributed Systems, 29(4):943–957.
Tang, J., Tay, W. P., and Wen, Y. (2014). Dynamic request redirection and elastic service scaling in cloud-centric media networks. IEEE Transactions on Multimedia, 16(5):1434–1445.
Tian, Y. et al. (2018). A new live video streaming approach based on Amazon S3 pricing model. In IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pages 321–328.
Wang, S. et al. (2015). Dynamic service migration in mobile edge-clouds. In IFIP Networking Conference (IFIP Networking), pages 1–9.
Xiao, W. et al. (2015). Dynamic request redirection and resource provisioning for cloud-based video services under heterogeneous environment. IEEE Transactions on Parallel and Distributed Systems, 27(7):1954–1967.
Zhang, Z.-H., Jiang, X.-F., and Xi, H.-S. (2016). Optimal content placement and request dispatching for cloud-based video distribution services. International Journal of Automation and Computing, 13(6):529–540.
Aazam, M. and Huh, E.-N. (2015). Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In 29th International Conference on Advanced Information Networking and Applications, pages 687–694.
Araújo, F., Rosário, D., Cerqueira, E., and Villas, L. A. (2019). A hybrid energy-aware video bitrate adaptation algorithm for mobile networks. In 15th Annual Conference on Wireless On-demand Network Systems and Services (WONS), pages 146–153. IEEE.
Bhardwaj, K. et al. (2019). Addressing the Fragmentation Problem in Distributed and Decentralized Edge Computing: A Vision. In IEEE International Conference on Cloud Engineering (IC2E), pages 156–167.
Chunlin, L. et al. (2019). Optimal media service selection scheme for mobile users in mobile cloud. Wireless Networks, 25(6):3179–3192.
Cisco (2019). Cisco visual networking index: Forecast and trends, 2017–2022. Technical report, Cisco.
He, Q. et al. (2017). Fog-based transcoding for crowdsourced video livecast. IEEE Communications Magazine, 55(4):28–33.
Jain, R. K., Chiu, D.-M. W., and Hawe, W. R. (1984). A quantitative measure of fairness and discrimination. Eastern Research Laboratory, Digital Equipment Corporation, Hudson, MA.
Jalali, F. et al. (2016). Fog computing may help to save energy in cloud computing. IEEE Journal on Selected Areas in Communications, 34(5):1728–1739.
Juluri, P., Tamarapalli, V., and Medhi, D. (2016). Measurement of Quality of Experience of Video-on-Demand Services: A Survey. IEEE Communications Surveys & Tutorials, 18(1):401–418.
Mao, Y. et al. (2017). A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Communications Surveys Tutorials, 19(4):2322–2358.
Masip-Bruin, X. et al. (2016). Foggy Clouds and Cloudy Fogs: a Real Need for Coordinated Management of Fog-to-Cloud Computing Systems. IEEE Wireless Communications, 23(5):120–128.
Menchaca-Mendez, R. et al. (2018). Opportunistic Mobile Sensing in the Fog. Wireless Communications and Mobile Computing, 2018:1–18.
Ni, L. et al. (2017). Resource allocation strategy in fog computing based on priced timed petrinets. IEEE Internet of Things Journal, 4(5):1216–1228.
Padhye, J. et al. (2000). Modeling tcp reno performance: a simple model and its empirical validation. IEEE/ACM transactions on Networking, 8(2):133–145.
Riley, G. F. and Henderson, T. R. (2010). The ns-3 network simulator. In Wehrle, K., Günes, M., and Gross, J., editors, Modeling and Tools for Network Simulation, pages 15–34. Springer.
Rosário, D. et al. (2018). Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support. Sensors, 18(2).
Salahuddin, M. A. et al. (2018). A survey on content placement algorithms for cloud-based content delivery networks. IEEE Access, 6:91–114.
Siavoshani, M. J., Pourmiri, A., and Shariatpanahi, S. P. (2017). Storage, communication, and load balancing trade-off in distributed cache networks. IEEE Transactions on Parallel and Distributed Systems, 29(4):943–957.
Tang, J., Tay, W. P., and Wen, Y. (2014). Dynamic request redirection and elastic service scaling in cloud-centric media networks. IEEE Transactions on Multimedia, 16(5):1434–1445.
Tian, Y. et al. (2018). A new live video streaming approach based on Amazon S3 pricing model. In IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pages 321–328.
Wang, S. et al. (2015). Dynamic service migration in mobile edge-clouds. In IFIP Networking Conference (IFIP Networking), pages 1–9.
Xiao, W. et al. (2015). Dynamic request redirection and resource provisioning for cloud-based video services under heterogeneous environment. IEEE Transactions on Parallel and Distributed Systems, 27(7):1954–1967.
Zhang, Z.-H., Jiang, X.-F., and Xi, H.-S. (2016). Optimal content placement and request dispatching for cloud-based video distribution services. International Journal of Automation and Computing, 13(6):529–540.
Publicado
07/12/2020
Como Citar
DE ALENCAR, Derian Fernando Alves; ROSÁRIO, Denis Lima; CERQUEIRA, Eduardo Coelho; BOTH, Cristiano Bonato; ANTUNES, Rodolfo Stoffel.
Distribuição de Conteúdo Sob Demanda Através da Alocação de Microserviços Dinâmicos na Borda e Núcleo da Rede. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 575-588.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12310.
