Experimental Framework to Simulate Rescue Operations after a Natural Disaster

• Veas Castillo, Luis ^[1] ; Ovando-Leon, Gabriel ^[1] ; Astudillo, Gabriel ^[3] ; Gil-Costa, Veronica ^[2] ; Marín, Mauricio ^[1]
  1. [1] Universidad de Santiago de Chile

     Universidad de Santiago de Chile

     Santiago, Chile

     
  2. [2] Universidad Nacional de San Luis

     Universidad Nacional de San Luis

     Argentina

     
  3. [3] Universidad de Valparaı́so, Valparaı́so, Chile

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• Localización: Journal of Computer Science and Technology, ISSN-e 1666-6038, Vol. 20, Nº. 2, 2020 (Ejemplar dedicado a: Fifty-Second Issue; e14)
• Idioma: inglés
• DOI: 10.24215/16666038.20.e07
• Títulos paralelos:
  • Framework Experimental para Simular Operaciones de Rescate despues de un Desatre Natural
• Enlaces
  • Texto completo
• Resumen
  • español

    La simulación computacional es una poderosa herramienta para evaluar el rendimiento de sistemas. Resulta útil para realizar el planeamiento de capacidad de clusters de Centros de Datos, para obtener perfiles de aplicaciones y detectar cuellos de botella. Se ha utilizado en diferentes áreas de investigación como buscadores web a gran escala, evacuaciones por desastres naturales, biología computacional, comportamiento y tendencia humana, entre otros. Sin embargo, ajustar correctamente los parámetros de los simuladores, definir los escenarios de simulación y recopilar los rastros de datos no es una tarea fácil. Es un proceso incremental que requiere contrastar constantemente las métricas estimadas y el flujo de acciones simuladas con datos reales. En este trabajo, presentamos el diseño de un marco experimental para el desarrollo de simulaciones a gran escala de aplicaciones sociales utilizadas después de un desastre natural. La primera es una aplicación social destinada a registrar voluntarios y gestionar campañas en emergencias y tareas. La segunda aplicación es un repositorio de datos llamado MongoDB.Las aplicaciones se depliegan en una plataforma distribuida que combina diferentes tecnologías como Proxy, Orquestador de Containers, Containers y una Base de Datos NoSQL. Simulamos ambas aplicaciones y la plataforma computational. Validamos nuestros simuladores utilizando trazas reales recopiladas durante simulacros.

  • English

    Computational simulation is a powerful tool for performance evaluation of computational systems. It is useful to make capacity planning of data center clusters, to obtain profiling reports of software applications and to detect bottlenecks. It has been used in different research areas like large scale Web search engines, natural disaster evacuations, computational biology, human behavior and tendency, among many others. However, properly tuning the parameters of the simulators, defining the scenarios to be simulated and collecting the data traces is not an easy task. It is an incremental process which requires constantly comparing the estimated metrics and the flow of simulated actions against real data. In this work, we present an experimental framework designed for the development of large scale simulations of two applications used upon the occurrence of a natural disaster strikes. The first one is a social application aimed to register volunteers and manage emergency campaigns and tasks. The second one is a benchmark application a data repository named MongoDB. The applications are deployed in a distributed platform which combines different technologies like a Proxy, a Containers Orchestrator, Containers and a NoSQL Database. We simulate both applications and the architecture platform. We validate our simulators using real traces collected during simulacrums of emergency situations.

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