SwePub - sökning: L773:9781450320375

Numrering	Referens	Omslagsbild	Hitta
1.	Bailis, P., et al. (författare) Bolt-on causal consistency 2013 Ingår i: SIGMOD '13 Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450320375 ; , s. 761-772 Konferensbidrag (refereegranskat)abstract We consider the problem of separating consistency-related safety properties from availability and durability in distributed data stores via the application of a "bolt-on" shim layer that upgrades the safety of an underlying general-purpose data store. This shim provides the same consistency guarantees atop a wide range of widely deployed but often inflexible stores. As causal consistency is one of the strongest consistency models that remain available during system partitions, we develop a shim layer that upgrades eventually consistent stores to provide convergent causal consistency. Accordingly, we leverage widely deployed eventually consistent infrastructure as a common substrate for providing causal guarantees. We describe algorithms and shim implementations that are suitable for a large class of application-level causality relationships and evaluate our techniques using an existing, production-ready data store and with real-world explicit causality relationships.

Bailis, P., et al. (författare)
Bolt-on causal consistency
2013
Ingår i: SIGMOD '13 Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450320375 ; , s. 761-772
Konferensbidrag (refereegranskat)abstract
- We consider the problem of separating consistency-related safety properties from availability and durability in distributed data stores via the application of a "bolt-on" shim layer that upgrades the safety of an underlying general-purpose data store. This shim provides the same consistency guarantees atop a wide range of widely deployed but often inflexible stores. As causal consistency is one of the strongest consistency models that remain available during system partitions, we develop a shim layer that upgrades eventually consistent stores to provide convergent causal consistency. Accordingly, we leverage widely deployed eventually consistent infrastructure as a common substrate for providing causal guarantees. We describe algorithms and shim implementations that are suitable for a large class of application-level causality relationships and evaluate our techniques using an existing, production-ready data store and with real-world explicit causality relationships.

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