Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
Using Global Behavior Modeling to Improve QoS in Cloud Data Storage Services
Year:2010
Research Areas
  • Artificial intelligence,
  • Information technology and adata processing
Information
Abstract
The cloud computing model aims to make largescale 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 reasonin about potential improvements. We apply our proposal to BlobSeer, 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.
International
Si
Congress
IEEE CloudCom 2010
960
Place
Indianapolis, US
Reviewers
Si
ISBN/ISSN
978-1-4244-9405-7
10.1109/CloudCom.2010.33
Start Date
30/11/2010
End Date
03/12/2010
From page
304
To page
311
Proceedings of CloudCom'2010
Participants
  • Autor: Maria de los Santos Perez Hernandez (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Ontology Engineering Group (LIA). Laboratorio Inteligencia Artificial. Grupo de Ingeniería Ontológica
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