Usando Deep Learning para o Escalonamento de Tarefas Comunicantes
Resumo
Este trabalho apresenta a proposta de um agente escalonador de tarefas comunicantes baseado em Deep Learning. O agente é treinado utilizando algoritmos conhecidos da literatura, amalgamando suas decisões em busca de generalizar o problema de escalonamento a partir da combinação dos processos de algoritmos simples e eficientes. Os resultados indicam que o agente escalonador obteve resultados competitivos comparado aos algoritmos tradicionais.
Palavras-chave:
Escalonamento e Balanceamento de Carga, Big Data
Referências
Aytug, H., Bhattacharyya, S., Koehler, G. J., and Snowdon, J. L. (1994). A review of machine learning in scheduling. IEEE Transactions on Engineering Management, 41(2):165–171.
Blenk, A., Kalmbach, P., Kellerer, W., and Schmid, S. (2017). o’zapft is: Tap your network algorithm’s big data! In Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, pages 19–24. ACM.
Cheng, M., Li, J., and Nazarian, S. (2018). Drl-cloud: Deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers. In Proceedings of the 23rd Asia and South Pacific Design Automation Conference, pages 129–134. IEEE Press.
Mao, H., Alizadeh, M., Menache, I., and Kandula, S. (2016). Resource management with deep reinforcement learning. In Proceedings of the 15th ACM Workshop on Hot Topics in Networks, pages 50–56. ACM.
Tripathy, B., Dash, S., and Padhy, S. K. (2015). Dynamic task scheduling using a directed neural network. Journal of Parallel and Distributed Computing, 75:101–106.
Vasile, M.-A., Florin, P., Mihaela-Cǎtǎlina, N., and Cristea, V. (2018). Mlbox: Machine learning box for asymptotic scheduling. Information Sciences, 433:401–416.
Weckman, G. R., Ganduri, C. V., and Koonce, D. A. (2008). A neural network job-shop scheduler. Journal of Intelligent Manufacturing, 19(2):191–201.
Woo, S., Yeon, J., Ji, M., Moon, I.-C., and Park, J. (2018). Deep reinforcement learning with fully convolutional neural network to solve an earthwork scheduling problem. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 4236–4242. IEEE.
Zhang, Q., Yang, L. T., Chen, Z., and Li, P. (2018). A survey on deep learning for big data. Information Fusion, 42:146–157.
Blenk, A., Kalmbach, P., Kellerer, W., and Schmid, S. (2017). o’zapft is: Tap your network algorithm’s big data! In Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, pages 19–24. ACM.
Cheng, M., Li, J., and Nazarian, S. (2018). Drl-cloud: Deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers. In Proceedings of the 23rd Asia and South Pacific Design Automation Conference, pages 129–134. IEEE Press.
Mao, H., Alizadeh, M., Menache, I., and Kandula, S. (2016). Resource management with deep reinforcement learning. In Proceedings of the 15th ACM Workshop on Hot Topics in Networks, pages 50–56. ACM.
Tripathy, B., Dash, S., and Padhy, S. K. (2015). Dynamic task scheduling using a directed neural network. Journal of Parallel and Distributed Computing, 75:101–106.
Vasile, M.-A., Florin, P., Mihaela-Cǎtǎlina, N., and Cristea, V. (2018). Mlbox: Machine learning box for asymptotic scheduling. Information Sciences, 433:401–416.
Weckman, G. R., Ganduri, C. V., and Koonce, D. A. (2008). A neural network job-shop scheduler. Journal of Intelligent Manufacturing, 19(2):191–201.
Woo, S., Yeon, J., Ji, M., Moon, I.-C., and Park, J. (2018). Deep reinforcement learning with fully convolutional neural network to solve an earthwork scheduling problem. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 4236–4242. IEEE.
Zhang, Q., Yang, L. T., Chen, Z., and Li, P. (2018). A survey on deep learning for big data. Information Fusion, 42:146–157.
Publicado
15/04/2020
Como Citar
PEREIRA, Kleiton; KOSLOVSKI, Guilherme Piêgas.
Usando Deep Learning para o Escalonamento de Tarefas Comunicantes. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DA REGIÃO SUL (ERAD-RS), 20. , 2020, Santa Maria.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 93-96.
ISSN 2595-4164.
DOI: https://doi.org/10.5753/eradrs.2020.10764.
