Achieving Causal Consistency under Partial Replication for Geo distributed Cloud Storage
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 stronger than eventual consistency semantics. 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.