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

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

Description

Title: Using Global Behavior Modeling to Improve QoS in Cloud Data Storage Services
Author/s:
  • Montes, Jesús
  • Nicolae, Bogdan
  • Antoniu, Gabriel
  • Sánchez, Alberto
  • Pérez Hernández, María de los Santos
Item Type: Presentation at Congress or Conference (Article)
Event Title: 2nd IEEE International Conference on Cloud Computing Technology and Science
Event Dates: 30/11/2010 - 03/12/2010
Event Location: Indianaplois, EEUU
Title of Book: Proceedings of 2nd IEEE International Conference on Cloud Computing Technology and Science
Date: February 2011
ISBN: 978-1-4244-9405-7
Subjects:
Faculty: E.U. de Informática (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 6853
DC Identifier: http://oa.upm.es/6853/
OAI Identifier: oai:oa.upm.es:6853
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5708464&tag=1
Deposited by: Memoria Investigacion
Deposited on: 06 May 2011 09:11
Last Modified: 20 Apr 2016 15:59
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