Full text
Preview |
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (208kB) | Preview |
Matri, Pierre, Pérez Hernández, María de los Santos ORCID: https://orcid.org/0000-0003-2949-3307, Costan, Alexandru, Bougé, Luc and Antoniu, Gabriel
(2017).
Keeping up with storage: decentralized, write-enabled dynamic geo-replication.
"Future Generation Computer Systems", v. 86
;
pp. 1093-1105.
ISSN 0167-739X.
https://doi.org/10.1016/j.future.2017.06.009.
Title: | Keeping up with storage: decentralized, write-enabled dynamic geo-replication |
---|---|
Author/s: |
|
Item Type: | Article |
Título de Revista/Publicación: | Future Generation Computer Systems |
Date: | 2017 |
ISSN: | 0167-739X |
Volume: | 86 |
Subjects: | |
Freetext Keywords: | Cloud; Replication; Geo-replication; Storage; Fault-tolerance; Consistency; Database; Key-value store |
Faculty: | E.T.S. de Ingenieros Informáticos (UPM) |
Department: | Arquitectura y Tecnología de Sistemas Informáticos |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
Preview |
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (208kB) | Preview |
Large-scale applications are ever-increasingly geo-distributed. Maintaining the highest possible data locality is crucial to ensure high performance of such applications. Dynamic replication addresses this problem by dynamically creating replicas of frequently accessed data close to the clients. This data is often stored in decentralized storage systems such as Dynamo or Voldemort, which offer support for mutable data. However, existing approaches to dynamic replication for such mutable data remain centralized, thus incompatible with these systems. In this paper we introduce a write-enabled dynamic replication scheme that leverages the decentralized architecture of such storage systems. We propose an algorithm enabling clients to locate tentatively the closest data replica without prior request to any metadata node. Large-scale experiments on various workloads show a read latency decrease of up to 42% compared to other state-of-the-art, caching-based solutions.
Item ID: | 50723 |
---|---|
DC Identifier: | https://oa.upm.es/50723/ |
OAI Identifier: | oai:oa.upm.es:50723 |
DOI: | 10.1016/j.future.2017.06.009 |
Official URL: | https://www.sciencedirect.com/science/article/pii/... |
Deposited by: | Memoria Investigacion |
Deposited on: | 21 Dec 2018 11:30 |
Last Modified: | 21 Dec 2018 11:30 |