Perfil de Consumo de Uma Aplicação de Reconhecimento Facial no Contexto de Computação de Borda
Resumo
Multi-Access Edge Computing (MEC) é um paradigma emergente que visa o provimento de recursos computacionais na borda das redes de telefonia celular, reduzindo a latência para usuários finais. O projeto de uma rede MEC envolve decisões sobre o dimensionamento dos recursos computacionais que serão empregados na borda da rede. Esse dimensionamento requer o conhecimento do perfil das aplicações ou serviç̧os que executarão na infraestrutura MEC. Neste trabalho, apresentamos os resultados de uma investigação sobre o perfil de consumo de recursos computacionais de uma aplicação de reconhecimento facial que executa em infraestruturas MEC. Para obtermos intuições práticas, utilizamos um software de código aberto denominado OpenFace.
Palavras-chave:
Multi-Access Edge Computing (MEC), recursos computacionais, redes de telefonia, Celular, latência
Referências
Abbas, N., Zhang, Y., Taherkordi, A., and Skeie, T. (2018). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1):450–465.
Amos, B., Ludwiczuk, B., and Satyanarayanan, M. (2016). Openface: A general-purpose face recognition library with mobile applications.
Ananthanarayanan, G., Bahl, P., Bod´ık, P., Chintalapudi, K., Philipose, M., Ravindranath, L., and Sinha, S. (2017). Real-time video analytics: The killer app for edge computing. IEEE Computing Society.
Baltrusaitis, T., Zadeh, A., Lim, Y. C., and Morency, L. (2018). Openface 2.0: Facial behavior analysis toolkit. In 2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018), pages 59–66.
Blondel, V. D., Esch, M., Chan, C., Clerot, F., Deville, P., Huens, E., Morlot, F., Smoreda, Z., and Ziemlicki, C. (2012). Data for Development: the D4D Challenge on Mobile Phone Data. arXiv e-prints, page arXiv:1210.0137.
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Coudurier, B., Hoyos, C. E., Niedermayer, M., and Mahol, P. B. (2019). Ffmpeg docu- mentation. https://www.ffmpeg.org/documentation.html.
Dautov, R., Distefano, S., Bruneo, D., Longo, F., Merlino, G., and Puliafito, A. (2018). Data processing in cyber-physical-social systems through edge computing. IEEE Ac- cess, 6:29822–29835.
Di Francesco, P., Malandrino, F., Forde, T. K., and DaSilva, L. A. (2015). A sharing- and competition-aware framework for cellular network evolution planning. IEEE Transac- tions on Cognitive Communications and Networking, 1(2):230–243.
Docker (2019). Docker documentation. https://docs.docker.com/get-started/. Online. dockerhub (2019). Openface. https://hub.docker.com/r/bamos/openface.
ETSI (2016). Multi access edge computing (mec). Technical report, European Telecom- munications Standards Intitute.
Garcia-Saavedra, A., Iosifidis, G., Costa-Perez, X., and Leith, D. J. (2018). Joint optimi- zation of edge computing architectures and radio access networks. IEEE Journal on Selected Areas in Communications, 36(11):2433–2443.
GitHub (2005–2016). V4l2loopback: Virtual video devices. https://github.com/umlaeute/ v4l2loopback.
Liu, H., Eldarrat, F., Alqahtani, H., Reznik, A., Foy, X., and Zhang, Y. (2018). Mobile edge cloud system: Architectures, challenges, and approaches. IEEE Systems Journal, 12(3):2495–2508.
Malandrino, F., Chiasserini, C., and Kirkpatrick, S. (2016). The price of fog: A data- driven study on caching architectures in vehicular networks. In Proceedings of the First International Workshop on Internet of Vehicles and Vehicles of Internet, pages 37–42.
Malandrino, F., Chiasserini, C.-F., Avino, G., Malinverno, M., and Kirkpatrick, S. (2018). From megabits to cpu ticks:enriching a demand trace in the age of mec. IEEE Tran- sactions On Big Data, pages 2332–7790.
Muslim, N. and Islam, S. (2017). Face recognition in the edge cloud. International Conference on Imaging, Signal Processing and Communication.
Parvez, I., Rahmati, A., Guvenc, I., Sarwat, A. I., and Dai, H. (2018). A survey on low latency towards 5g: Ran, core network and caching solutions. IEEE Communications Surveys Tutorials, 20(4):3098–3130.
Patel, M., Joubert, J., Ramos, J. R., Sprecher, N., Abeta, S., and Neal, A. (2014). Mo- bile edge computing - introductory technical white paper. Technical report, European Telecommunications Standards.
Satyanarayanan, M., Bahl, P., Caceres, R., and Davies, N. (2009). The case for vm-based cloudlets in mobile computing. IEEE Pervasive Computing, 8(4):14–23.
Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., and Sabella, D. (2017). On multi- access edge computing: A survey of the emerging 5g network edge cloud architecture and orchestration. IEEE Communications Surveys Tutorials, 19(3):1657–1681.
Wuytack, S., Catthoor, F., Nachtergaele, L., and Man, H. D. (1996). Power exploration for data dominated ’video applications. International Symposium on Low Power Eletronics and Design.
Zhang, S., Xu, X., Wu, Y., and Lu, L. (2014). 5g: Towards energy-efficient, low-latency and high-reliable communications networks. In 2014 IEEE International Conference on Communication Systems, pages 197–201.
Amos, B., Ludwiczuk, B., and Satyanarayanan, M. (2016). Openface: A general-purpose face recognition library with mobile applications.
Ananthanarayanan, G., Bahl, P., Bod´ık, P., Chintalapudi, K., Philipose, M., Ravindranath, L., and Sinha, S. (2017). Real-time video analytics: The killer app for edge computing. IEEE Computing Society.
Baltrusaitis, T., Zadeh, A., Lim, Y. C., and Morency, L. (2018). Openface 2.0: Facial behavior analysis toolkit. In 2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018), pages 59–66.
Blondel, V. D., Esch, M., Chan, C., Clerot, F., Deville, P., Huens, E., Morlot, F., Smoreda, Z., and Ziemlicki, C. (2012). Data for Development: the D4D Challenge on Mobile Phone Data. arXiv e-prints, page arXiv:1210.0137.
CISCO (2019). Cisco visual networking index: Global mobile data traffic fore- cast update, 2017–2022 white paper. https://www.cisco.com/c/en/us/solutions/ collateral/service-provider/visual-networking-index-vni/white-paper-c11-738429. html# Toc953326.
Coudurier, B., Hoyos, C. E., Niedermayer, M., and Mahol, P. B. (2019). Ffmpeg docu- mentation. https://www.ffmpeg.org/documentation.html.
Dautov, R., Distefano, S., Bruneo, D., Longo, F., Merlino, G., and Puliafito, A. (2018). Data processing in cyber-physical-social systems through edge computing. IEEE Ac- cess, 6:29822–29835.
Di Francesco, P., Malandrino, F., Forde, T. K., and DaSilva, L. A. (2015). A sharing- and competition-aware framework for cellular network evolution planning. IEEE Transac- tions on Cognitive Communications and Networking, 1(2):230–243.
Docker (2019). Docker documentation. https://docs.docker.com/get-started/. Online. dockerhub (2019). Openface. https://hub.docker.com/r/bamos/openface.
ETSI (2016). Multi access edge computing (mec). Technical report, European Telecom- munications Standards Intitute.
Garcia-Saavedra, A., Iosifidis, G., Costa-Perez, X., and Leith, D. J. (2018). Joint optimi- zation of edge computing architectures and radio access networks. IEEE Journal on Selected Areas in Communications, 36(11):2433–2443.
GitHub (2005–2016). V4l2loopback: Virtual video devices. https://github.com/umlaeute/ v4l2loopback.
Liu, H., Eldarrat, F., Alqahtani, H., Reznik, A., Foy, X., and Zhang, Y. (2018). Mobile edge cloud system: Architectures, challenges, and approaches. IEEE Systems Journal, 12(3):2495–2508.
Malandrino, F., Chiasserini, C., and Kirkpatrick, S. (2016). The price of fog: A data- driven study on caching architectures in vehicular networks. In Proceedings of the First International Workshop on Internet of Vehicles and Vehicles of Internet, pages 37–42.
Malandrino, F., Chiasserini, C.-F., Avino, G., Malinverno, M., and Kirkpatrick, S. (2018). From megabits to cpu ticks:enriching a demand trace in the age of mec. IEEE Tran- sactions On Big Data, pages 2332–7790.
Muslim, N. and Islam, S. (2017). Face recognition in the edge cloud. International Conference on Imaging, Signal Processing and Communication.
Parvez, I., Rahmati, A., Guvenc, I., Sarwat, A. I., and Dai, H. (2018). A survey on low latency towards 5g: Ran, core network and caching solutions. IEEE Communications Surveys Tutorials, 20(4):3098–3130.
Patel, M., Joubert, J., Ramos, J. R., Sprecher, N., Abeta, S., and Neal, A. (2014). Mo- bile edge computing - introductory technical white paper. Technical report, European Telecommunications Standards.
Satyanarayanan, M., Bahl, P., Caceres, R., and Davies, N. (2009). The case for vm-based cloudlets in mobile computing. IEEE Pervasive Computing, 8(4):14–23.
Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., and Sabella, D. (2017). On multi- access edge computing: A survey of the emerging 5g network edge cloud architecture and orchestration. IEEE Communications Surveys Tutorials, 19(3):1657–1681.
Wuytack, S., Catthoor, F., Nachtergaele, L., and Man, H. D. (1996). Power exploration for data dominated ’video applications. International Symposium on Low Power Eletronics and Design.
Zhang, S., Xu, X., Wu, Y., and Lu, L. (2014). 5g: Towards energy-efficient, low-latency and high-reliable communications networks. In 2014 IEEE International Conference on Communication Systems, pages 197–201.
Publicado
22/11/2019
Como Citar
RODRIGUES, Karlla Bianca C.; CARDOSO, Kleber Vieira ; CORRÊA, Sand Luz .
Perfil de Consumo de Uma Aplicação de Reconhecimento Facial no Contexto de Computação de Borda. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 7. , 2019, Goiânia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 369-378.
