Using Global Behavior Modeling to Improve QoS in Cloud Data Storage Services

Montes, Jesús; Nicolae, Bogdan; Antoniu, Gabriel; Sánchez, Alberto y Pérez Hernández, María de los Santos (2011). Using Global Behavior Modeling to Improve QoS in Cloud Data Storage Services. En: "2nd IEEE International Conference on Cloud Computing Technology and Science", 30/11/2010 - 03/12/2010, Indianaplois, EEUU. ISBN 978-1-4244-9405-7.

Descripción

Título: Using Global Behavior Modeling to Improve QoS in Cloud Data Storage Services
Autor/es:
  • Montes, Jesús
  • Nicolae, Bogdan
  • Antoniu, Gabriel
  • Sánchez, Alberto
  • Pérez Hernández, María de los Santos
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 2nd IEEE International Conference on Cloud Computing Technology and Science
Fechas del Evento: 30/11/2010 - 03/12/2010
Lugar del Evento: Indianaplois, EEUU
Título del Libro: Proceedings of 2nd IEEE International Conference on Cloud Computing Technology and Science
Fecha: Febrero 2011
ISBN: 978-1-4244-9405-7
Materias:
Escuela: E.U. de Informática (UPM) [antigua denominación]
Departamento: Arquitectura y Tecnología de Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The cloud computing model aims to make large-scale data-intensive computing affordable even for users with limited financial resources, that cannot invest into expensive infrastructures necesssary to run them. In this context, MapReduce is emerging as a highly scalable programming paradigm that enables high-throughput data-intensive processing as a cloud service. Its performance is highly dependent on the underlying storage service, responsible to efficiently support massively parallel data accesses by guaranteeing a high throughput under heavy access concurrency. In this context, quality of service plays a crucial role: the storage service needs to sustain a stable throughput for each individual accesss, in addition to achieving a high aggregated throughput under concurrency. In this paper we propose a technique to address this problem using component monitoring, application-side feedback and behavior pattern analysis to automatically infer useful knowledge about the causes of poor quality of service and provide an easy way to reason in about potential improvements. We apply our proposal to Blob Seer, a representative data storage service specifically designed to achieve high aggregated throughputs and show through extensive experimentation substantial improvements in the stability of individual data read accesses under MapReduce workloads.

Más información

ID de Registro: 6853
Identificador DC: http://oa.upm.es/6853/
Identificador OAI: oai:oa.upm.es:6853
URL Oficial: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5708464&tag=1
Depositado por: Memoria Investigacion
Depositado el: 06 May 2011 09:11
Ultima Modificación: 20 Abr 2016 15:59
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