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Área de la Ciencia:

  • Ciencias de la Computación
  • Sistemas Distribuidos
  • Inteligencia Artificial

Sus antecedentes:

  • Los despliegues de Internet de las cosas (IoT) enfrentan desafíos en latencia, costo y utilización de recursos.
  • Las estrategias de descarga tradicionales a menudo descuidan las capas intermedias y la movilidad de los dispositivos, lo que genera ineficiencias.
  • Existe la necesidad de marcos avanzados para optimizar la asignación de recursos en entornos IoT dinámicos.

Objetivo del estudio:

  • Proponer y evaluar Public Edge as a Service (PEaaS) como un nivel intermedio para la descarga de IoT.
  • Desarrollar un marco de simulación, RegionalEdgeSimPy, para modelar y evaluar la arquitectura PEaaS.
  • Optimizar las decisiones de descarga de tareas considerando la latencia, el costo, la congestión y la energía.

Principales métodos:

  • Desarrollado RegionalEdgeSimPy, un simulador de Python para el marco PEaaS.
  • Implementado un programador Proximal Policy Optimization (PPO) que incorpora la movilidad y múltiples parámetros de entrada.
  • Utilizada la enmascaración de acciones y una función de recompensa multiobjetivo para decisiones de descarga inteligentes.
  • Realizadas simulaciones con 10 a 3000 dispositivos en un entorno de ciudad inteligente.

Principales resultados:

  • La programación PPO prioriza el procesamiento en el borde local hasta la sobreutilización, luego dirige las tareas a PEaaS.
  • PEaaS maneja de manera efectiva las cargas de trabajo descargadas, con un uso mínimo de los recursos en la nube.
  • Utilización promedio: Borde (75,8%), PEaaS (52,9%), Nube (<1,2%).

Conclusiones:

  • El marco PEaaS basado en PPO reduce significativamente el retraso, el costo y las fallas de las tareas en los despliegues de IoT.
  • El sistema propuesto demuestra una escalabilidad mejorada para manejar la movilidad en el procesamiento de macrodatos de IoT.
  • PEaaS ofrece una capa intermedia eficiente para la gestión distribuida de recursos de IoT.