  Maintaining causal consistency in distributed shared memory systems using
vector timestamps has received a lot of attention from both theoretical and
practical prospective. However, most of the previous literature focuses on full
replication where each data is stored in all replicas, which may not be
scalable due to the increasing amount of data. In this report, we investigate
how to achieve causal consistency in partial replicated systems, where each
replica may store different set of data. We propose an algorithm that tracks
causal dependencies via vector timestamp in client-server model for partial
replication. The cost of our algorithm in terms of timestamps size varies as a
function of the manner in which the replicas share data, and the set of
replicas accessed by each client. We also establish a connection between our
algorithm with the previous work on full replication.
