Causal consistency algorithms for partially replicated and fully replicated systems

Data replication is commonly used for fault-tolerance in reliable distributed systems. In large-scale systems, it additionally provides low latency. Recently, causal consistency in such systems has received much attention. However, existing works assume the data is fully replicated. This greatly simplifies the design of the algorithms to implement causal consistency. In this paper, we propose that it can be advantageous to have partial replication of data, and we propose two algorithms for achieving causal consistency in such systems where the data is only partially replicated. This work provides the first evidence that explores causal consistency for partially replicated distributed systems. We also give a special case algorithm for causal consistency in the full-replication case. We give simulation results to show the performance of our algorithms, and to present the advantage of partial replication over full replication.