Balanceamento de carga na borda da rede usando blockchain das coisas
Resumo
A computação em névoa tem sido usada como infraestrutura de processamento para aplicações IoT, trazendo benefícios como baixa latência para a aplicação. Entretanto, características como o dinamismo, a distribuição e a heterogeneidade dos ambientes de névoa trazem à tona vários desafios no tocante ao gerenciamento dos recursos disponíveis. Nesse contexto, apresentamos um mecanismo para gerenciamento de recursos e balanceamento de carga de forma descentralizada e autônoma. O mecanismo baseia-se no uso associado de blockchain e dos princípios da computação autonômica para lidar com os desafios do gerenciamento na névoa. Os resultados mostram que o mecanismo proporciona uma distribuição da capacidade de processamento na borda, mesmo em caso de falhas.
Palavras-chave:
balanceamento de carga, computação de borda, computação em névoa, blockchain, internet das coisas
Referências
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Bellavista, P., Berrocal, J., Corradi, A., Das, S. K., Foschini, L., and Zanni, A. (2019). A survey on fog computing for the internet of things. Pervasive and Mobile Computing, 52:71–99.
Buterin, V. (2013). Ethereum whitepaper. Technical report, Ethereum.
Buyya, R. and Dastjerdi, A. V. (2016). Internet of Things: Principles and paradigms. Elsevier.
Cisco (2015). Fog computing and the internet of things: Extend the cloud to where the things are. Technical report, Cisco Internet Business Solutions Group (IBSG).
IBM (2005). White paper: An architectural blueprint for autonomic computing. Technical report, IBM Corporation.
Li, C., Zhuang, H., Wang, Q., and Zhou, X. (2018). SSLB: Self-similarity-based load balancing for large-scale fog computing. Arabian Journal for Science and Engineering, 43(12):7487–7498.
Manasrah, A., Aldomi, A., and Gupta, B. B. (2019). An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment. Cluster Computing, 22.
Pesic, S., Tosic, M., Ikovi´c, O., Ivanovic, M., Radovanovic, M., and Boscovic, D. (2018). Context Aware Resource and Service Provisioning Management in Fog Computing Systems, volume 737, pages 213–223. .
Pinto Neto, E. C., Callou, G., and Aires, F. (2017). An algorithm to optimise the load distribution of fog environments. In IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC), pages 1292–1297.
Prazeres, C. and Serrano, M. (2016). Soft-iot: Self-organizing fog of things. In 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pages 803–808.
SONG Ningning, GONG Chao, A. X. Z. Q. (2016). Fog computing dynamic load balancing mechanism based on graph repartitioning. China Communications, 13(3):156.
Sousa, N. R. e Prazeres, C. (2018). M2-fot: a proposal for monitoring and management of fog of things platforms. IEEE Symposium on Computers and Communications.
Swan, M. (2015). Blockchain: Blueprint for a new economy. ”O’Reilly Media, Inc.”.
Verma, S. K., Yadav, A. K., Motwani, D., Raw, R. S., and Singh, H. K. (2016). An efficient data replication and load balancing technique for fog computing environment. Int. Conf. on Computing for Sustainable Global Development, pages 2888–2895.
Xu, X., Fu, S., Cai, Q., Tian,W., Liu,W., Dou,W., Sun, X., and Liu, A. (2018). Dynamic resource allocation for load balancing in fog environment. Wireless Communications and Mobile Computing, 2018:1–15.
Bellavista, P., Berrocal, J., Corradi, A., Das, S. K., Foschini, L., and Zanni, A. (2019). A survey on fog computing for the internet of things. Pervasive and Mobile Computing, 52:71–99.
Buterin, V. (2013). Ethereum whitepaper. Technical report, Ethereum.
Buyya, R. and Dastjerdi, A. V. (2016). Internet of Things: Principles and paradigms. Elsevier.
Cisco (2015). Fog computing and the internet of things: Extend the cloud to where the things are. Technical report, Cisco Internet Business Solutions Group (IBSG).
IBM (2005). White paper: An architectural blueprint for autonomic computing. Technical report, IBM Corporation.
Li, C., Zhuang, H., Wang, Q., and Zhou, X. (2018). SSLB: Self-similarity-based load balancing for large-scale fog computing. Arabian Journal for Science and Engineering, 43(12):7487–7498.
Manasrah, A., Aldomi, A., and Gupta, B. B. (2019). An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment. Cluster Computing, 22.
Pesic, S., Tosic, M., Ikovi´c, O., Ivanovic, M., Radovanovic, M., and Boscovic, D. (2018). Context Aware Resource and Service Provisioning Management in Fog Computing Systems, volume 737, pages 213–223. .
Pinto Neto, E. C., Callou, G., and Aires, F. (2017). An algorithm to optimise the load distribution of fog environments. In IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC), pages 1292–1297.
Prazeres, C. and Serrano, M. (2016). Soft-iot: Self-organizing fog of things. In 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pages 803–808.
SONG Ningning, GONG Chao, A. X. Z. Q. (2016). Fog computing dynamic load balancing mechanism based on graph repartitioning. China Communications, 13(3):156.
Sousa, N. R. e Prazeres, C. (2018). M2-fot: a proposal for monitoring and management of fog of things platforms. IEEE Symposium on Computers and Communications.
Swan, M. (2015). Blockchain: Blueprint for a new economy. ”O’Reilly Media, Inc.”.
Verma, S. K., Yadav, A. K., Motwani, D., Raw, R. S., and Singh, H. K. (2016). An efficient data replication and load balancing technique for fog computing environment. Int. Conf. on Computing for Sustainable Global Development, pages 2888–2895.
Xu, X., Fu, S., Cai, Q., Tian,W., Liu,W., Dou,W., Sun, X., and Liu, A. (2018). Dynamic resource allocation for load balancing in fog environment. Wireless Communications and Mobile Computing, 2018:1–15.
Publicado
30/06/2020
Como Citar
COSTA, Alexandre; ROCHA, Atslands; DELICATO, Flávia; SOUZA, Jose.
Balanceamento de carga na borda da rede usando blockchain das coisas. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 12. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 91-100.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2020.11215.
