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Un nuevo marco de programación de tareas en la nube que utiliza el aprendizaje de refuerzo profundo jerárquico para
Delong Cui1, Zhiping Peng2, Kaibin Li1
1College of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming, China.
PloS one
|August 21, 2025
Resumen
Este estudio introduce un marco jerárquico de aprendizaje de refuerzo profundo (DRL) para la programación eficiente de tareas en la nube. El programador DRL optimiza los costos y el rendimiento, mejora el equilibrio de carga y reduce las tareas atrasadas en un 10%.
Área de la Ciencia:
- Ciencias de la computación
- Inteligencia artificial
- La computación en la nube
Sus antecedentes:
- La programación de tareas de computación en la nube es NP-completa debido a cargas grandes y dinámicas.
- Los métodos existentes luchan con la eficiencia y la adaptabilidad en entornos dinámicos de nube.
Objetivo del estudio:
- Proponer un nuevo marco jerárquico de aprendizaje de refuerzo profundo (DRL) para la programación de tareas en la nube a gran escala.
- Mejorar la adaptabilidad, la rentabilidad y el rendimiento en entornos dinámicos de nube.
Principales métodos:
- Un enfoque de programación jerárquica que asigna tareas primero a grupos de máquinas virtuales y luego a máquinas virtuales individuales.
- Un programador basado en DRL que aprende y adapta continuamente los parámetros de la red.
Principales resultados:
- El marco DRL equilibra efectivamente el costo y el rendimiento, optimizando el equilibrio de carga, el costo y el tiempo de vencimiento.
- Logró una mejora general del 10% en comparación con los algoritmos heurísticos clásicos.
- Reducción de costes demostrada en escenarios de baja carga y mejora de la utilización de los recursos en escenarios de alta carga.
Conclusiones:
- El marco de DRL jerárquico propuesto ofrece una solución prometedora para los desafíos complejos de programación de tareas en la nube.
- Las limitaciones reconocidas incluyen sobrecarga computacional, latencia potencial y dependencia de datos.
- Se necesita más investigación para abordar la complejidad y mejorar la eficiencia en tiempo real.
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