An adaptive rank-based approach for dynamic controller selection in Fog Computing
Resumo
The deployment of a dynamic and cooperative fog control plane, where controllers are selected on-demand among the most suitable underlying resources, has been recently proposed as a Control-as-a-Service (CaaS) model. Albeit it is expected that real-time applications shall benefit from this concept, mechanisms for QoS-aware controllers election is yet an open issue. In this work, we propose an adaptive rank-based controller selection strategy that is capable of tuning the weights employed for each characteristic of interest in
order to cope with the environment dynamism. Results have shown an average controller exchange reduction of 37% when compared with a preliminary approach employing fixed weights in a dynamic scenario, as well as its efficiency in battery and memory usage by controllers.
Palavras-chave:
Fog Computing, Ranking, Dynamic controller selection, Iot, Control-as-a-Service
Referências
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Anawar, M. R., Wang, S., Zia, M. A., Jadoon, A. K., Akram, U., and Raza, S. (2018). Fog computing: An overview of big iot data analytics. Wireless Communications and Mobile Computing, 2018:7157192:1–7157192:22.
Arkian, H. R., Atani, R. E., and Pourkhalili, A. (2014). A cluster-based vehicular cloud architecture with learning-based resource management. In 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, pages 162–167.
Athwani, P. and Vidyarthi, D. P. (2015). Resource discovery in mobile cloud computing: A clustering based approach. In 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON), pages 1–6.
Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (2012). Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, MCC ’12, pages 13–16, New York, NY, USA. ACM.
Byers, C. C. (2017). Architectural imperatives for fog computing: Use cases, requirements, and architectural techniques for fog-enabled iot networks. IEEE Communications Magazine, 55(8):14–20.
Cheng, T. Y., Wang, M., and Jia, X. (2015). QoS-guaranteed controller placement in SDN. In 2015 IEEE Global Communications Conference (GLOBECOM), pages 1–6.
Costa, M. V. S., Souza, V. B., and Júnior, S. S. A. (2019). Dynamic control-as-a-service provisioning in fog computing. In 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pages 1–6.
Ericsson (2017). Ericsson mobility report. https://www.ericsson.com/assets/local/mobility-report/documents/2017/ ericsson-mobility-report-june-2017.pdf. Accessed: 2019-10-08.
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Jiang, Y., Huang, Z., and Tsang, D. H. K. (2018). Challenges and solutions in fog computing orchestration. IEEE Network, 32(3):122–129.
Jiménez, Y., Cervelló-Pastor, C., and Garcı́a, A. (2015). Dynamic resource discovery protocol for software defined networks. IEEE Communications Letters, 19(5):743–746.
Kim, D., Lee, H., Song, H., Choi, N., and Yi, Y. (2018). On the economics of fog computing: Inter-play among infrastructure and service providers, users, and edge resource owners. In 2018 IEEE International Conference on Communications (ICC), pages 1–6.
Lee, G., Saad, W., and Bennis, M. (2017). An online secretary framework for fog network formation with minimal latency. In 2017 IEEE International Conference on Communications (ICC), pages 1–6.
Salsano, S., Siracusano, G., Detti, A., Pisa, C., Ventre, P. L., and Blefari-Melazzi, N. (2014). Controller selection in a wireless mesh SDN under network partitioning and merging scenarios. CoRR, abs/1406.2470.
Souza, V. B., Gomez, A., Masip-Bruin, X., Marin-Tordera, E., and Garcia, J. (2017). Towards a fog-to-cloud control topology for qos-aware end-to-end communication. In 2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS), pages 1–5.
Sridharan, V., Gurusamy, M., and Truong-Huu, T. (2017). On multiple controller mapping in software defined networks with resilience constraints. IEEE Communications Letters, 21(8):1763– 1766.
Anawar, M. R., Wang, S., Zia, M. A., Jadoon, A. K., Akram, U., and Raza, S. (2018). Fog computing: An overview of big iot data analytics. Wireless Communications and Mobile Computing, 2018:7157192:1–7157192:22.
Arkian, H. R., Atani, R. E., and Pourkhalili, A. (2014). A cluster-based vehicular cloud architecture with learning-based resource management. In 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, pages 162–167.
Athwani, P. and Vidyarthi, D. P. (2015). Resource discovery in mobile cloud computing: A clustering based approach. In 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON), pages 1–6.
Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (2012). Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, MCC ’12, pages 13–16, New York, NY, USA. ACM.
Byers, C. C. (2017). Architectural imperatives for fog computing: Use cases, requirements, and architectural techniques for fog-enabled iot networks. IEEE Communications Magazine, 55(8):14–20.
Cheng, T. Y., Wang, M., and Jia, X. (2015). QoS-guaranteed controller placement in SDN. In 2015 IEEE Global Communications Conference (GLOBECOM), pages 1–6.
Costa, M. V. S., Souza, V. B., and Júnior, S. S. A. (2019). Dynamic control-as-a-service provisioning in fog computing. In 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pages 1–6.
Ericsson (2017). Ericsson mobility report. https://www.ericsson.com/assets/local/mobility-report/documents/2017/ ericsson-mobility-report-june-2017.pdf. Accessed: 2019-10-08.
Foundation, L. (2018). Linux foundation wiki: netem. https://wiki.linuxfoundation.org/networking/netem. Accessed: 2019-10-14.
Jiang, Y., Huang, Z., and Tsang, D. H. K. (2018). Challenges and solutions in fog computing orchestration. IEEE Network, 32(3):122–129.
Jiménez, Y., Cervelló-Pastor, C., and Garcı́a, A. (2015). Dynamic resource discovery protocol for software defined networks. IEEE Communications Letters, 19(5):743–746.
Kim, D., Lee, H., Song, H., Choi, N., and Yi, Y. (2018). On the economics of fog computing: Inter-play among infrastructure and service providers, users, and edge resource owners. In 2018 IEEE International Conference on Communications (ICC), pages 1–6.
Lee, G., Saad, W., and Bennis, M. (2017). An online secretary framework for fog network formation with minimal latency. In 2017 IEEE International Conference on Communications (ICC), pages 1–6.
Salsano, S., Siracusano, G., Detti, A., Pisa, C., Ventre, P. L., and Blefari-Melazzi, N. (2014). Controller selection in a wireless mesh SDN under network partitioning and merging scenarios. CoRR, abs/1406.2470.
Souza, V. B., Gomez, A., Masip-Bruin, X., Marin-Tordera, E., and Garcia, J. (2017). Towards a fog-to-cloud control topology for qos-aware end-to-end communication. In 2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS), pages 1–5.
Sridharan, V., Gurusamy, M., and Truong-Huu, T. (2017). On multiple controller mapping in software defined networks with resilience constraints. IEEE Communications Letters, 21(8):1763– 1766.
Publicado
07/12/2020
Como Citar
COSTA, Marcus Vinícius Souza; SOUZA, Vitor Barbosa.
An adaptive rank-based approach for dynamic controller selection in Fog Computing. 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. 113-126.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12277.
