Karma: Cost-Effective Geo-Replicated Cloud Storage with Dynamic Enforcement of Causal Consistency

Causal consistency has emerged as an attractive middle-ground to architecting cloud storage systems, as it allows for high availability and low latency, while supporting semantics stronger than eventual consistency. However, causally-consistent cloud storage systems have seen limited deployment in practice. A key factor is these systems employ full replication of all the data in all the data centers (
DCs
), incurring high cost. A simple extension of current causal systems to support partial replication by clustering
DCs
into
rings
incurs availability and latency problems. We propose
Karma
, the first system to enable causal consistency for partitioned data stores while achieving the cost advantages of partial replication
without
the availability and latency problems of the simple extension. Our evaluation with 64 servers emulating 8 geo-distributed
DCs
shows that
Karma
(i) incurs much lower cost than a fully-replicated causal store (obviously due to the lower replication factor); and (ii) offers higher availability and better performance than the above partial-replication extension at similar costs.