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A correlation-aware data placement strategy for key-value stores

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Detalhes bibliográficos
Resumo:Key-value stores hold the unprecedented bulk of the data produced by applications such as social networks. Their scalability and availability requirements often outweigh sacri cing richer data and pro- cessing models, and even elementary data consistency. Moreover, existing key-value stores have only random or order based placement strategies. In this paper we exploit arbitrary data relations easily expressed by the application to foster data locality and improve the performance of com- plex queries common in social network read-intensive workloads. We present a novel data placement strategy, supporting dynamic tags, based on multidimensional locality-preserving mappings. We compare our data placement strategy with the ones used in existing key-value stores under the workload of a typical social network appli- cation and show that the proposed correlation-aware data placement strategy o ers a major improvement on the system's overall response time and network requirements.
Autores principais:Vilaça, Ricardo
Outros Autores:Oliveira, Rui Carlos Mendes de; Pereira, José, 1973-
Assunto:Peer-to-peer DHT Cloud Computing Dependability
Ano:2011
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso aberto
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho
Descrição
Resumo:Key-value stores hold the unprecedented bulk of the data produced by applications such as social networks. Their scalability and availability requirements often outweigh sacri cing richer data and pro- cessing models, and even elementary data consistency. Moreover, existing key-value stores have only random or order based placement strategies. In this paper we exploit arbitrary data relations easily expressed by the application to foster data locality and improve the performance of com- plex queries common in social network read-intensive workloads. We present a novel data placement strategy, supporting dynamic tags, based on multidimensional locality-preserving mappings. We compare our data placement strategy with the ones used in existing key-value stores under the workload of a typical social network appli- cation and show that the proposed correlation-aware data placement strategy o ers a major improvement on the system's overall response time and network requirements.