Evaluación del uso de redes neuronales artificiales para predecir el rendimiento de aplicaciones distribuidas independientemente de la plataforma

Jesus Flores-Contreras; Hector Duran-Limon; Efren Mezura-Montes

doi:10.29105/vtga3.3-1119

Autores/as

Jesus Flores-Contreras Universidad Autónoma de Nuevo León
Hector Duran-Limon University of Guadalajara
Efren Mezura-Montes Universidad Veracruzana

DOI:

https://doi.org/10.29105/vtga3.3-1119

Palabras clave:

Sistemas distribuidos, Plataforma independiente, Predicción del tiempo de ejecución, Técnica de aprendizaje automático, Aplicaciones de larga duración

Resumen

Un sistema distribuido es un conjunto de sistemas informáticos independientes interconectados por una red, que trabajan en cooperación y se comportan como un único sistema creando una plataforma subyacente para distintos tipos de aplicaciones. Estas plataformas suelen utilizarse para ejecutar aplicaciones de larga duración que requieren muchos recursos informáticos, como potencia de procesamiento de la CPU, memoria y ancho de banda de la red. En estos sistemas, es importante gestionar los recursos disponibles de forma eficiente para mejorar el rendimiento general del sistema. Saber cómo se va a comportar el tiempo de ejecución de una aplicación puede mejorar mucho el rendimiento de un sistema, ya que esta información permite asignar de forma eficiente los recursos disponibles. En este artículo, presentamos una evaluación de la idoneidad de las redes neuronales artificiales para lograr un enfoque independiente de la plataforma para la predicción del tiempo de ejecución de aplicaciones distribuidas que se ejecutan en sistemas multinúcleo. Realizamos nuestra evaluación con tres aplicaciones paralelas de larga ejecución, a saber, el modelo de Investigación y Predicción Meteorológica (WRF), Octopus y miniFE. Nuestros resultados indican que las redes neuronales son capaces de producir resultados precisos cuando predicen el tiempo de ejecución de la aplicación en la misma plataforma, pero su precisión disminuye al cambiar de plataforma.

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