Data replication strategies in grid environments

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Abstract
Data grids provide geographically distributed resources for large-scale data-intensive applications that generate large data sets. However, ensuring efficient and fast access to such huge and widely distributed data is hindered by the high latencies of the Internet. To address these problems we introduce a set of replication management services and protocols that offer high data availability, low bandwidth consumption, increased fault tolerance, and improved scalability of the overall system. Replication decisions are made based on a cost model that evaluates data access costs and performance gains of creating each replica. The estimation of costs and gains is based on factors such as run-time accumulated read/write statistics, response time, bandwidth, and replica size. To address scalability, replicas are organized in a combination of hierarchical and flat topologies that represent propagation graphs that minimize inter-replica communication costs. To evaluate our model we use the network simulator NS to study the impact of replication. Our results prove that replication improves the performance of data access on the data grid, and that the gain increases with the size of data used.

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