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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Conjunto de datos sobre asignación y uso de recursos para una nube privada

Paola Marques1, Mariana Mendes1, Thiago Emmanuel Pereira1

  • 1Department of Computing and Systems, Federal University of Campina Grande, Campina Grande, Brazil.

Data in brief
|February 18, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Los investigadores publicaron un nuevo conjunto de datos que detalla el uso de la nube privada OpenStack. Este valioso recurso, con más de 64 millones de registros, ayuda en la investigación de computación en la nube y el análisis de nubes privadas.

Palabras clave:
IaaSMonitorización de infraestructuraOpenStackCaracterización de cargas de trabajo

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Área de la Ciencia:

  • Ciencias de la Computación
  • Computación en la Nube
  • Ciencia de Datos

Sus antecedentes:

  • Los proveedores de nube pública dominan comercialmente, pero las nubes privadas son esenciales para las instituciones académicas y de investigación debido a necesidades específicas de gobernanza.
  • Los conjuntos de datos existentes sobre el uso de recursos en la nube se centran principalmente en las nubes públicas, dejando una brecha en la comprensión de los entornos de nube privada.
  • Esta escasez limita la investigación sobre los patrones operativos y las estrategias de optimización de las nubes privadas.

Objetivo del estudio:

  • Presentar un conjunto de datos completo sobre el uso de recursos de una nube privada basada en OpenStack.
  • Proporcionar un recurso valioso para los investigadores que estudian entornos de nube privada y su dinámica.
  • Facilitar estudios sobre asignación de recursos, utilización y rendimiento del sistema en entornos de nube no comerciales.

Principales métodos:

  • Se recopilaron más de 64 millones de registros de una nube privada OpenStack durante casi doce meses (del 23 de mayo de 2024 al 16 de mayo de 2025).
  • Se consultaron periódicamente las API de OpenStack y los servicios de monitorización cada cinco minutos para recopilar datos.
  • Se anonimizaron los atributos sensibles, conservando solo los UUID generados por el sistema para garantizar la privacidad.

Principales resultados:

  • El conjunto de datos incluye detalles de la infraestructura, cuotas de asignación, asociaciones de usuario a proyecto, especificaciones de máquinas virtuales y métricas de utilización de recursos.
  • Las entradas con marcas de tiempo permiten el análisis temporal de la dinámica del sistema de la nube privada.
  • El conjunto de datos ofrece una vista detallada y con marcas de tiempo de los aspectos operativos de una nube privada.

Conclusiones:

  • Este conjunto de datos cierra la brecha en los datos disponibles públicamente de entornos de nube privada no comerciales.
  • Sirve como un recurso valioso para instituciones académicas y empresas que exploran la repatriación de la nube.
  • Los hallazgos respaldan investigaciones adicionales sobre la gestión, optimización y seguridad de las nubes privadas.