An offline demand estimation method for multi-threaded applications

Juan F. Perez, Sergio Pacheco-Sanchez, Giuliano Casale

Producción científica: Capítulo en Libro/ReporteContribución a la conferencia

18 Citas (Scopus)

Resumen

Parameterizing performance models for multi-threaded enterprise applications requires finding the service rates offered by worker threads to the incoming requests. Statistical inference on monitoring data is here helpful to reduce the overheads of application profiling and to infer missing information. While linear regression of utilization data is often used to estimate service rates, it suffers erratic performance and also ignores a large part of application monitoring data, e.g., response times. Yet inference from other metrics, such as response times or queue-length samples, is complicated by the dependence on scheduling policies. To address these issues, we propose novel scheduling-aware estimation approaches for multi-threaded applications based on linear regression and maximum likelihood estimators. The proposed methods estimate demands from samples of the number of requests in execution in the worker threads at the admission instant of a new request. Validation results are presented on simulated and real application datasets for systems with multi-class requests, class switching, and admission control.

Idioma originalInglés estadounidense
Título de la publicación alojadaProceedings - 2013 IEEE 21st International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication, MASCOTS 2013
Páginas21-30
Número de páginas10
DOI
EstadoPublicada - dic. 1 2013
Publicado de forma externa
Evento2013 IEEE 21st International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication, MASCOTS 2013 - San Francisco, CA, Estados Unidos
Duración: ago. 14 2013ago. 16 2013

Conferencia

Conferencia2013 IEEE 21st International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication, MASCOTS 2013
País/TerritorioEstados Unidos
CiudadSan Francisco, CA
Período8/14/138/16/13

Áreas temáticas de ASJC Scopus

  • Ingeniería eléctrica y electrónica
  • Redes de ordenadores y comunicaciones
  • Software
  • Modelización y simulación

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