Modelo Estocástico para Avaliação de Disponibilidade de Hospitais Inteligentes
Resumo
Smart hospitals need local and remote servers to efficiently process and store data. However, there is a significant difficulty in assessing the availability of such systems in real contexts, because failures are not tolerated and the cost of prototyping is high. This paper adopts Stochastic Petri Nets (SPNs) to assess the availability of an smart hospital system, avoiding premature investment in real equipment. In addition, this work presents a sensitivity analysis that identifies the most critical architecture components. The proposed model has the potential to assist hospital systems administrators in planning more optimized architectures according to their needs.
Palavras-chave:
hospital inteligente, disponibilidade, redes de Petri
Referências
Araujo, E., Dantas, J., Matos, R., Pereira, P., and Maciel, P. (2019). Dependability evaluation of an iot system: A hierarchical modelling approach. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pages 2121–2126. IEEE.
Araujo, J., Silva, B., Oliveira, D., and Maciel, P. (2014). Dependability evaluation of amhealth system using a mobile cloud infrastructure. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 1348–1353.
Campolongo, F., Tarantola, S., and Saltelli, A. (1999). Tackling quantitatively large dimensionality problems. Computer Physics Communication, 117(1):75–85.
Campolongo, F., Tarantola, S., Saltelli, A., and Ratto, M. (2004).Sensitivity analysis in practice: a guide to assessing scientific models. John Wiley and Sons.Chen,
L. and Ha, W. (2018). Reliability prediction and qos selection for web service composition. IJCSE, 16(2):202–211.
da Silva Lisboa, M. F. F., Santos, G. L., Lynn, T., Sadok, D., Kelner, J., Endo, P. T., et al.(2018). Modeling the availability of an e-health system integrated with edge, fog and cloud infrastructures. In 2018 IEEE Symposium on Computers and Communications(ISCC), pages 00416–00421. IEEE.
de Souza Matos Júnior, R. (2016). Identification of Availability and Performance Bottlenecks in Cloud Computing Systems: An Approach Based On Hierarchical Models and Sensitivity Analysis. PhD thesis, Federal University of Pernambuco, Center forInformatics, Graduate in Computer Science, Recife.
Farahani, B., Firouzi, F., and Chakrabarty, K. (2020). Healthcare iot. In Intelligent Internet of Things, pages 515–545. Springer.
Gomez-Sacristan, A., Rodriguez-Hernandez, M. A., and Sempere, V. (2015). Evaluation of quality of service in smart-hospital communications. Journal of Medical Imagingand Health Informatics, 5(8):1864–1869.
Hoffman, F. and Gardner, R. (1983). Evaluation of Uncertainties in Environmental Radiological Assessment Models. Radiological Assessments.
Kim, S.-M. (2018). A study on the design plan of ux for the smart healthcare for the aged society-focused on iot technology. The Journal of the Korea Contents Association,18(11):462–474.
Pantelopoulos, A. and Bourbakis, N. (2009). Spn-model based simulation of a wearable health monitoring system. 31st Annual International Conference of the IEEE EMBS.
Pianosi, F., Beven, K., Freer, J., Hall, J., Rougier, J., Stephenson, D., and Wagener, T.(2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling and Software, 79:214–232.
Rahmani, A. M., Gia, T. N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., and Lilje-berg, P. (2018). Exploiting smart e-health gateways at the edge of healthcare internet-of-things: A fog computing approach.Future G. C. S., 78:641–658.
Rahmani, A.-M., Thanigaivelan, N. K., Gia, T. N., Granados, J., Negash, B., Liljeberg, P.,and Tenhunen, H. (2015). Smart e-health gateway: Bringing intelligence to internet-of-things based ubiquitous healthcare systems. In 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), pages 826–834. IEEE.
Rodrigues, L., Endo, P. T., and Silva, F. A. (2019). Stochastic model for evaluating smarthospitals performance. In 2019 IEEE LATINCOM, pages 1–6. IEEE.
Santos, G., Gomes, D., Kelner, J., Sadok, D., Silva, F., Endo, P., and Lynn, T. (2018a).The internet of things for health care: Optimizing e-health system availability in the fog and cloud. Int. J. of Computational Science and Engineering, pages 1–13.
Santos, G. L., Endo, P. T., da Silva Lisboa, M. F. F., da Silva, L. G. F., Sadok, D., Kelner,J., Lynn, T., et al. (2018b). Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructures. Journal of Cloud Compu-ting, 7(1):16.
Silva, B., Matos, R., Callou, G., Figueiredo, J., Oliveira, D., Ferreira, J., Dantas, J.,Lobo, A., Alves, V., and Maciel, P. (2015a). Mercury: An integrated environment for performance and dependability evaluation of general systems. In Proceedings of Industrial Track at 45th Dependable Systems and Networks Conference, DSN.
Silva, F. A., Kosta, S., Rodrigues, M., Oliveira, D., Maciel, T., Mei, A., and Maciel, P.(2017). Mobile cloud performance evaluation using stochastic models.IEEE Transac-tions on Mobile Computing, 17(5):1134–1147.
Silva, F. A., Rodrigues, M., Maciel, P., Kosta, S., and Mei, A. (2015b). Planning mobile cloud infrastructures using stochastic petri nets and graphic processing units. In 2015 IEEE 7th International Conference on Cloud Computing Technology and Science(CloudCom), pages 471–474. IEEE.
Tigre, M., Santos, G., Lynn, T., Sadok, D., Kelner, J., and Endo, P. (2018). Modeling theavailability of an e-health system integrated with edge, fog and cloud infrastructures. IEEE ISCC, pages 416–421.
Wong, J., Goh, Q. Y., Tan, Z., Lie, S. A., Tay, Y. C., Ng, S. Y., and Soh, C. R. (2020).Preparing for a covid-19 pandemic: a review of operating room outbreak response measures in a large tertiary hospital in singapore. Canadian Journal of Anesthesia/Journalcanadien d’anesth ́esie, pages 1–14.
Xiaoli, W., Guangju, C., Quiang, Z., and Zhongping, G. (2007). Reduction of stochastic petri nets for reliability analysis. The Eighth International Conference on Electronic Measurement and Instruments.
Zhang, H., Li, J., Wen, B., Xun, Y., and Liu, J. (2018). Connecting intelligent things insmart hospitals using nb-iot.IEEE Internet of Things Journal, 5(3):1550–1560.
Araujo, J., Silva, B., Oliveira, D., and Maciel, P. (2014). Dependability evaluation of amhealth system using a mobile cloud infrastructure. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 1348–1353.
Campolongo, F., Tarantola, S., and Saltelli, A. (1999). Tackling quantitatively large dimensionality problems. Computer Physics Communication, 117(1):75–85.
Campolongo, F., Tarantola, S., Saltelli, A., and Ratto, M. (2004).Sensitivity analysis in practice: a guide to assessing scientific models. John Wiley and Sons.Chen,
L. and Ha, W. (2018). Reliability prediction and qos selection for web service composition. IJCSE, 16(2):202–211.
da Silva Lisboa, M. F. F., Santos, G. L., Lynn, T., Sadok, D., Kelner, J., Endo, P. T., et al.(2018). Modeling the availability of an e-health system integrated with edge, fog and cloud infrastructures. In 2018 IEEE Symposium on Computers and Communications(ISCC), pages 00416–00421. IEEE.
de Souza Matos Júnior, R. (2016). Identification of Availability and Performance Bottlenecks in Cloud Computing Systems: An Approach Based On Hierarchical Models and Sensitivity Analysis. PhD thesis, Federal University of Pernambuco, Center forInformatics, Graduate in Computer Science, Recife.
Farahani, B., Firouzi, F., and Chakrabarty, K. (2020). Healthcare iot. In Intelligent Internet of Things, pages 515–545. Springer.
Gomez-Sacristan, A., Rodriguez-Hernandez, M. A., and Sempere, V. (2015). Evaluation of quality of service in smart-hospital communications. Journal of Medical Imagingand Health Informatics, 5(8):1864–1869.
Hoffman, F. and Gardner, R. (1983). Evaluation of Uncertainties in Environmental Radiological Assessment Models. Radiological Assessments.
Kim, S.-M. (2018). A study on the design plan of ux for the smart healthcare for the aged society-focused on iot technology. The Journal of the Korea Contents Association,18(11):462–474.
Pantelopoulos, A. and Bourbakis, N. (2009). Spn-model based simulation of a wearable health monitoring system. 31st Annual International Conference of the IEEE EMBS.
Pianosi, F., Beven, K., Freer, J., Hall, J., Rougier, J., Stephenson, D., and Wagener, T.(2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling and Software, 79:214–232.
Rahmani, A. M., Gia, T. N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., and Lilje-berg, P. (2018). Exploiting smart e-health gateways at the edge of healthcare internet-of-things: A fog computing approach.Future G. C. S., 78:641–658.
Rahmani, A.-M., Thanigaivelan, N. K., Gia, T. N., Granados, J., Negash, B., Liljeberg, P.,and Tenhunen, H. (2015). Smart e-health gateway: Bringing intelligence to internet-of-things based ubiquitous healthcare systems. In 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), pages 826–834. IEEE.
Rodrigues, L., Endo, P. T., and Silva, F. A. (2019). Stochastic model for evaluating smarthospitals performance. In 2019 IEEE LATINCOM, pages 1–6. IEEE.
Santos, G., Gomes, D., Kelner, J., Sadok, D., Silva, F., Endo, P., and Lynn, T. (2018a).The internet of things for health care: Optimizing e-health system availability in the fog and cloud. Int. J. of Computational Science and Engineering, pages 1–13.
Santos, G. L., Endo, P. T., da Silva Lisboa, M. F. F., da Silva, L. G. F., Sadok, D., Kelner,J., Lynn, T., et al. (2018b). Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructures. Journal of Cloud Compu-ting, 7(1):16.
Silva, B., Matos, R., Callou, G., Figueiredo, J., Oliveira, D., Ferreira, J., Dantas, J.,Lobo, A., Alves, V., and Maciel, P. (2015a). Mercury: An integrated environment for performance and dependability evaluation of general systems. In Proceedings of Industrial Track at 45th Dependable Systems and Networks Conference, DSN.
Silva, F. A., Kosta, S., Rodrigues, M., Oliveira, D., Maciel, T., Mei, A., and Maciel, P.(2017). Mobile cloud performance evaluation using stochastic models.IEEE Transac-tions on Mobile Computing, 17(5):1134–1147.
Silva, F. A., Rodrigues, M., Maciel, P., Kosta, S., and Mei, A. (2015b). Planning mobile cloud infrastructures using stochastic petri nets and graphic processing units. In 2015 IEEE 7th International Conference on Cloud Computing Technology and Science(CloudCom), pages 471–474. IEEE.
Tigre, M., Santos, G., Lynn, T., Sadok, D., Kelner, J., and Endo, P. (2018). Modeling theavailability of an e-health system integrated with edge, fog and cloud infrastructures. IEEE ISCC, pages 416–421.
Wong, J., Goh, Q. Y., Tan, Z., Lie, S. A., Tay, Y. C., Ng, S. Y., and Soh, C. R. (2020).Preparing for a covid-19 pandemic: a review of operating room outbreak response measures in a large tertiary hospital in singapore. Canadian Journal of Anesthesia/Journalcanadien d’anesth ́esie, pages 1–14.
Xiaoli, W., Guangju, C., Quiang, Z., and Zhongping, G. (2007). Reduction of stochastic petri nets for reliability analysis. The Eighth International Conference on Electronic Measurement and Instruments.
Zhang, H., Li, J., Wen, B., Xun, Y., and Liu, J. (2018). Connecting intelligent things insmart hospitals using nb-iot.IEEE Internet of Things Journal, 5(3):1550–1560.
Publicado
30/06/2020
Como Citar
RODRIGUES, Laécio; GONÇALVES, Igor; FÉ, Iure; ENDO, Patrícia; SILVA, Francisco Airton.
Modelo Estocástico para Avaliação de Disponibilidade de Hospitais Inteligentes. In: WORKSHOP EM DESEMPENHO DE SISTEMAS COMPUTACIONAIS E DE COMUNICAÇÃO (WPERFORMANCE), 19. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 145-156.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2020.11113.
