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

Authors

Jesus Flores-Contreras Autonomous University of Nuevo León
Hector Duran-Limon University of Guadalajara
Efren Mezura-Montes University of Veracruz

DOI:

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

Keywords:

Distributed systems, Platform-independent, Prediction of runtime, Machine Learning technique, Long-running applications

Abstract

A distributed system is a set of independent computer systems interconnected by a network, which work in a cooperative way and behave as a single system creating an underlying platform for different kinds of applications. These platforms are usually used to execute long-running applications that demand a lot of computational resources e.g. CPU processing power, memory, and network bandwidth. In such kind of systems it is important to manage the available resources in an efficient way in order to improve the system’s overall performance. Knowing how an application’s runtime is going to behave can greatly improve performance of a system, since this information allows the efficient distribution of available resources. In this work, we present an evaluation of the suitability of artificial neural networks to achieve a platform-independent approach to execution time prediction of distributed applications running on multi-core systems. We performed our evaluation with three parallel long-running applications, namely the Weather Research and Forecasting (WRF) model, Octopus, and miniFE. Our results indicate that neural networks are capable of producing accurate results when predicting the application runtime on the same platform, but its accuracy decreases when the platform is changed

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