A Correlation-Aware Data Placement Strategy for Key-Value Stores

• Main Authors: Vilaça, Ricardo, Oliveira, Rui, Pereira, José
• Other Authors: Universidade do Minho = University of Minho Braga, Pascal Felber, Romain Rouvoy, TC 6, WG 6.1
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2011
• Subjects: Peer-to-Peer; DHT; Cloud Computing; Dependability; [INFO]Computer Science [cs]; [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]; Iceland
• Online Access: https://inria.hal.science/hal-01583587; https://inria.hal.science/hal-01583587/document; https://inria.hal.science/hal-01583587/file/978-3-642-21387-8_17_Chapter.pdf; https://doi.org/10.1007/978-3-642-21387-8_17

International audience Key-value stores hold the unprecedented bulk of the data produced by applications such as social networks. Their scalability and availability requirements often outweigh sacrificing richer data and processing 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 complex 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 application and show that the proposed correlation-aware data placement strategy offers a major improvement on the system’s overall response time and network requirements.