Improving Performance Estimation of Smart City Simulations Using the Actor Model

• Francisco Wallison Rocha USP
• Emilio Francesquini UFABC
• Daniel Cordeiro USP

DOI: https://doi.org/10.5753/eradsp.2024.239855

Resumo

The United Nations estimates that the world will reach around 10.4 billion people by 2050. Urban mobility problems already faced by large cities will be worsened, such as the emission of polluting gases into the atmosphere. These problems require innovative solutions. Solutions within the context of smart cities emerge as an alternative, an example of which is simulations. However, large-scale simulations are still a challenge. Techniques such as SimEDaPE emerge to help face these challenges. For this reason, they must be robust techniques to deal with a large volume of data. Therefore, this work presents a new approach using the actor-based model to improve the performance of SimEDaPE. The approach proposed here proved to be 48× than its predecessors.

Referências

Berndt, D. J. and Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAIWS’94, page 359–370, Seattle, WA. AAAI Press.

Hong, S., man Hui, E. C., and Lin, Y. (2022). Relationship between urban spatial structure and carbon emissions: A literature review. Ecological Indicators, 144:109456.

Mattson, T. G., Anderson, T. A., and Georgakoudis, G. (2021). Pyomp: Multithreaded parallel programming in python. Computing in Science Engineering, 23(6):77–80.

Rocha, F., Francesquini, E., and Cordeiro, D. (2022a). Fast simedape: Simulation estimation by data patterns exploration. In Anais da XIII Escola Regional de Alto Desempenho de São Paulo, pages 37–40, Porto Alegre, RS, Brasil. SBC.

Rocha, F., Francesquini, E., and Cordeiro, D. (2022b). Improving smart city simulation performance with simedape and parallelism. In Anais do XXI Workshop em Desempenho de Sistemas Computacionais e de Comunicação, pages 108–113, Porto Alegre, RS, Brasil. SBC.

Rocha, F. W., Fukuda, J. C., Francesquini, E., and Cordeiro, D. (2021). Accelerating smart city simulations. Latin America High Performance Computing Conference. To publish.

Salvador, S. and Chan, P. (2007). Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis, 11(5):561–580.

Santana, E. F. Z., Lago, N., Kon, F., and Milojicic, D. S. (2017). InterSCSimulator: Large-scale traffic simulation in smart cities using erlang. In International Workshop on Multi-Agent Systems and Agent-Based Simulation, pages 211–227. Springer.

Taamneh, S., Qawasmeh, A., and Aljammal, A. H. (2020). Parallel and fault-tolerant kmeans clustering based on the actor model. Multiagent and Grid Systems, 16(4):379–396.