Attribute-Based Partial Geo-Replication System

Existing partial geo-replication systems do not always provide optimal cost or latency, because their replication decisions are based on statically established data access popularity metrics, regardless of the application types. We demonstrate that additional reduction in cost and latency can be achieved by 1) using the right object attributes for making replication decisions for each type of application, 2) using multi-attribute-based replications, and 3) combining the popularity-based but reactive approach with the more random but proactive approach to data replication. Toward this end, we propose Acorn, an Attribute-based COntinuous partial geo-ReplicatioN system, and its prototype implementation based on Apache Cassandra. Experiments with two types of global-scale, data-sharing applications demonstrate up to 54% and 90% cost overhead reduction over existing systems or 38% and 91% latency overhead reduction.