Escalonamento com Consciência Energética para Fluxos de Trabalho Científicos sem Servidor: Uma Abordagem de Aprendizado de Máquina

• Lucas Rosa USP
• Alfredo Goldman USP

DOI: https://doi.org/10.5753/eradsp.2024.239934

Resumo

Este artigo propõe abordar os desafios de eficiência energética e escalonamento de fluxos de trabalho científicos em ambientes de computação sem servidor. Integrando técnicas de aprendizado de máquina e simulação, a pesquisa visa preencher lacunas entre eficiência energética e escalonamento sem servidor. A metodologia inclui coleta de dados históricos, previsão de consumo energético por meio de aprendizado de máquina e desenvolvimento de políticas de escalonamento com redes neurais profundas. O projeto também envolve adaptação de sistemas de gerenciamento de fluxo de trabalho e validação em ambientes reais, visando oferecer soluções viáveis para os desafios atuais em HPC.

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