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Control descentralizado de la cola con desplazamiento de retraso en Edge-IoT utilizando aprendizaje de refuerzo

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Este resumen es generado por máquina.

Este estudio introduce un modelo adaptativo para los sistemas de IoT de borde para gestionar los servicios de solicitud de manera eficiente. Mejora la calidad del servicio (QoS) y la eficiencia energética, incluso con tráfico inestable, ajustando dinámicamente los tiempos de procesamiento.

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Área de la Ciencia:

  • Ciencias de la computación
  • Ingeniería eléctrica
  • Matemáticas aplicadas

Sus antecedentes:

  • Los sistemas Edge-IoT se enfrentan a desafíos con crecientes demandas de eficiencia energética, capacidad de respuesta y autorregulación.
  • Las condiciones de tráfico inestables en las redes periféricas requieren estrategias de gestión de servicios adaptativas.
  • Los modelos existentes a menudo carecen de flexibilidad para manejar los requisitos dinámicos de calidad de servicio y gestión de la energía.

Objetivo del estudio:

  • Desarrollar un enfoque adaptativo para el modelado y la gestión de los procesos de servicio de solicitudes en los nodos periféricos de los sistemas Edge-IoT.
  • Mejorar la eficiencia energética, la capacidad de respuesta y la autorregulación en condiciones de tráfico fluctuantes.
  • Proporcionar una solución escalable y independiente del tipo de tráfico para la calidad de servicio dinámica y la gestión de la energía.

Principales métodos:

  • Se propuso un modelo estocástico G/G/1 con un desplazamiento de tiempo parametrizado para tener en cuenta la indisponibilidad del dispositivo.
  • Las expresiones analíticas para los indicadores de calidad de servicio (retraso, variabilidad, pérdida, consumo de energía) se derivaron como funciones del parámetro de desplazamiento.
  • Se implementó un agente de aprendizaje de refuerzo basado en Deep Q-Network (DQN) para el control descentralizado en tiempo real del parámetro de cambio.

Principales resultados:

  • Demostró una reducción en el retraso promedio en un 17-26% en comparación con los modelos de última generación.
  • Se ha logrado una disminución de las fluctuaciones en el tiempo de servicio y una mejor estabilidad de recuperación de la cola después de las cargas máximas.
  • La solución propuesta es agnóstica del tipo de tráfico y escalable a través de diversas arquitecturas de borde.

Conclusiones:

  • El enfoque adaptativo modela y gestiona de manera efectiva los procesos de servicio en los sistemas IoT de vanguardia, mejorando la calidad de servicio y la eficiencia energética.
  • El agente basado en DQN proporciona un control dinámico y descentralizado, adaptándose a los estados de cola en tiempo real.
  • Los hallazgos son aplicables a las redes de sensores, los escenarios de borde 5G/6G y los sistemas que requieren QoS dinámica y gestión de la energía.