Memorias de investigación
Ponencias en congresos:
Harmony: Towards Automated Self-Adaptive Consistency in Cloud Storage
Año:2012
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
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.
Internacional
Si
Nombre congreso
IEEE International Conference on Cluster Computing 2012
Tipo de participación
960
Lugar del congreso
Beijing, China
Revisores
Si
ISBN o ISSN
0000000000
DOI
Fecha inicio congreso
24/09/2012
Fecha fin congreso
28/09/2012
Desde la página
293
Hasta la página
301
Título de las actas
IEEE Computer Society Press
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Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Ontology Engineering Group