Correlated Data Placement in Distributed Systems

In distributed systems, communication cost is one of the major concerns. Many existing research have been conducted on placement of data to reduce communication cost and improve performance in widely distributed systems. All these research works focus on independent data objects. However, data are correlated due to accesses from clients and the correlation has some impact on date placement. In our previous research, we addressed the replica placement issue on correlated data to achieve improved performance for systems supporting mobile transactions. In internet environment, additional issues need to be considered. In this paper, we first show that replica allocation decisions can be made locally for each replica site in a tree network, with data access knowledge of its neighbors. We then develop a new replication cost model for correlated data objects in Internet environment. Based on the cost model and the algorithms in previous research, we develop a distributed optimal replica allocation algorithm (DOPR) for correlated data in internet environment. A distributed heuristic algorithm (DHPR) is then developed to efficiently make replica placement decisions. The algorithm obtains near optimal solutions for the correlated data model and yields significant performance gains. Experimental studies show that the distributed heuristic allocation algorithm significantly outperforms the general frequency-based replication schemes.