Harmony: Towards automated self-adaptive consistency in cloud storage

Pérez Hernández, María de los Santos; Chihoub, Houssem-Eddine; Ibrahim, Shadi y Antoniu, Gabriel (2012). Harmony: Towards automated self-adaptive consistency in cloud storage. En: "CLUSTER - IEEE International Conference on Cluster Computing 2012", 24/09/2012 - 28/09/2012, Beijing, China. ISBN 978-1-4673-2422-9. pp. 293-301.

Descripción

Título: Harmony: Towards automated self-adaptive consistency in cloud storage
Autor/es:
  • Pérez Hernández, María de los Santos
  • Chihoub, Houssem-Eddine
  • Ibrahim, Shadi
  • Antoniu, Gabriel
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: CLUSTER - IEEE International Conference on Cluster Computing 2012
Fechas del Evento: 24/09/2012 - 28/09/2012
Lugar del Evento: Beijing, China
Título del Libro: CLUSTER - IEEE International Conference on Cluster Computing 2012
Fecha: 2012
ISBN: 978-1-4673-2422-9
Materias:
Palabras Clave Informales: consistency, replications, data stale, Cassandra, cloud, self-adaptive, coherencia, réplicas, datos obsoletos, nube, auto-adaptativo.
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Arquitectura y Tecnología de Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

In just a few years cloud computing has become a very popular paradigm and a business success story, with storage being one of the key features. To achieve high data availability, cloud storage services rely on replication. In this context, one major challenge is data consistency. In contrast to traditional approaches that are mostly based on strong consistency, many cloud storage services opt for weaker consistency models in order to achieve better availability and performance. This comes at the cost of a high probability of stale data being read, as the replicas involved in the reads may not always have the most recent write. In this paper, we propose a novel approach, named Harmony, which adaptively tunes the consistency level at run-time according to the application requirements. The key idea behind Harmony is an intelligent estimation model of stale reads, allowing to elastically scale up or down the number of replicas involved in read operations to maintain a low (possibly zero) tolerable fraction of stale reads. As a result, Harmony can meet the desired consistency of the applications while achieving good performance. We have implemented Harmony and performed extensive evaluations with the Cassandra cloud storage on Grid?5000 testbed and on Amazon EC2. The results show that Harmony can achieve good performance without exceeding the tolerated number of stale reads. For instance, in contrast to the static eventual consistency used in Cassandra, Harmony reduces the stale data being read by almost 80% while adding only minimal latency. Meanwhile, it improves the throughput of the system by 45% while maintaining the desired consistency requirements of the applications when compared to the strong consistency model in Cassandra.

Más información

ID de Registro: 19574
Identificador DC: http://oa.upm.es/19574/
Identificador OAI: oai:oa.upm.es:19574
URL Oficial: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6337791
Depositado por: Memoria Investigacion
Depositado el: 16 Oct 2013 14:30
Ultima Modificación: 21 Abr 2016 20:20
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