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.
@cite_16: To provide high availability for services such as mail or bulletin boards, data must be replicated. One way to guarantee consistency of replicated data is to force service operations to occur in the same order at all sites, but this approach is expensive. For some applications a weaker causal operation order can preserve consistency while providing better performance. This paper describes a new way of implementing causal operations. Our technique also supports two other kinds of operations: operations that are totally ordered with respect to one another and operations that are totally ordered with respect to all other operations. The method performs well in terms of response time, operation-processing capacity, amount of stored state, and number and size of messages; it does better than replication methods based on reliable multicast techniques.
* Full replication Lazy Replication @cite_16 is a classic framework for providing causal consistency in distributed shared memory, where all clients and replicas both maintain a vector clock of size equal to the number of replicas. Clients can issue updates and queries to any replica, and replicas exchange gossip messages to keep their data up-to-date.