Towards efficient localization of dynamic replicas for Geo-Distributed data stores

Costan, Alexandru; Matri, Pierre; Antoniu, Gabriel; Montes Sánchez, Jesús y Pérez Hernández, María de los Santos (2016). Towards efficient localization of dynamic replicas for Geo-Distributed data stores. En: "7th Workshop on Scientific Cloud Computing", 01 Jun 2016, Kyoto, Japón. ISBN 978-1-4503-4353-4. pp. 3-9. https://doi.org/10.1145/2913712.2913715.

Descripción

Título: Towards efficient localization of dynamic replicas for Geo-Distributed data stores
Autor/es:
  • Costan, Alexandru
  • Matri, Pierre
  • Antoniu, Gabriel
  • Montes Sánchez, Jesús
  • Pérez Hernández, María de los Santos
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 7th Workshop on Scientific Cloud Computing
Fechas del Evento: 01 Jun 2016
Lugar del Evento: Kyoto, Japón
Título del Libro: ScienceCloud'16: Proceedings of the ACM 7th Workshop on Scientific Cloud Computing
Fecha: 2016
ISBN: 978-1-4503-4353-4
Volumen: 1
Materias:
Palabras Clave Informales: Storage Networks; Geo-Replication; Wide-area replication; Content Distribution Network; Data Warehousing; Meta- data; Data Consistency; Cloud; Availability
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Arquitectura y Tecnología de Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
Pdf - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (786kB) | Vista Previa

Resumen

Large-scale scientific experiments increasingly rely on geo- distributed clouds to serve relevant data to scientists world- wide with minimal latency. State-of-the-art caching systems often require the client to access the data through a caching proxy, or to contact a metadata server to locate the closest available copy of the desired data. Also, such caching sys- tems are inconsistent with the design of distributed hash- table databases such as Dynamo, which focus on allowing clients to locate data independently. We argue there is a gap between existing state-of-the-art solutions and the needs of geographically distributed applications, which require fast access to popular objects while not degrading access latency for the rest of the data. In this paper, we introduce a proba- bilistic algorithm allowing the user to locate the closest copy of the data e?ciently and independently with minimal over- head, allowing low-latency access to non-cached data. Also, we propose a network-e?cient technique to identify the most popular data objects in the cluster and trigger their replica- tion close to the clients. Experiments with a real-world data set show that these principles allow clients to locate the clos- est available copy of data with small memory footprint and low error-rate, thus improving read-latency for non-cached data and allowing hot data to be read locally.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Horizonte 2020642963BigStorageUNIVERSIDAD POLITECNICA DE MADRIDBigStorage: Storage-based Convergence between HPC and Cloud to handle Big Data

Más información

ID de Registro: 45071
Identificador DC: http://oa.upm.es/45071/
Identificador OAI: oai:oa.upm.es:45071
Identificador DOI [BETA]: 10.1145/2913712.2913715
URL Oficial: http://dl.acm.org/citation.cfm?doid=2913712.2913715
Depositado por: Memoria Investigacion
Depositado el: 09 Mar 2017 15:55
Ultima Modificación: 09 Mar 2017 15:55
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM