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

Costan, Alexandru and Matri, Pierre and Antoniu, Gabriel and Montes Sánchez, Jesús and Pérez Hernández, María de los Santos (2016). Towards efficient localization of dynamic replicas for Geo-Distributed data stores. In: "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.

Description

Title: Towards efficient localization of dynamic replicas for Geo-Distributed data stores
Author/s:
  • Costan, Alexandru
  • Matri, Pierre
  • Antoniu, Gabriel
  • Montes Sánchez, Jesús
  • Pérez Hernández, María de los Santos
Item Type: Presentation at Congress or Conference (Article)
Event Title: 7th Workshop on Scientific Cloud Computing
Event Dates: 01 Jun 2016
Event Location: Kyoto, Japón
Title of Book: ScienceCloud'16: Proceedings of the ACM 7th Workshop on Scientific Cloud Computing
Date: 2016
ISBN: 978-1-4503-4353-4
Volume: 1
Subjects:
Freetext Keywords: Storage Networks; Geo-Replication; Wide-area replication; Content Distribution Network; Data Warehousing; Meta- data; Data Consistency; Cloud; Availability
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

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

More information

Item ID: 45071
DC Identifier: http://oa.upm.es/45071/
OAI Identifier: oai:oa.upm.es:45071
DOI: 10.1145/2913712.2913715
Official URL: http://dl.acm.org/citation.cfm?doid=2913712.2913715
Deposited by: Memoria Investigacion
Deposited on: 09 Mar 2017 15:55
Last Modified: 09 Mar 2017 15:55
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