Adaptive Replica Placement in Hierarchical Data Grids

Data grids support distributed data-intensive applications that need to access massive (multi-terabyte or larger) datasets stored around the world. Ensuring efficient and fast access to such widely distributed datasets is hindered by the high latencies of wide-area networks. To speed up access, data files can be replicated so users can access nearby copies. Replication also provides high data availability, decreased bandwidth consumption, increased fault tolerance, and improved scalability. Since a grid environment is highly dynamic, resource availability, network latency, and users requests may change frequently. To address these issues a dynamic replica placement strategy that adapts to dynamic behavior in data grids is needed. In this paper, we extend our earlier work on popularity-based replica placement proposing a new adaptive algorithm for use in large-scale hierarchical data grids. Our algorithm dynamically adapts the frequency and degree of replication based on data access arrival rate and available storage capacities. We evaluate our algorithm using OptorSim. Our results show that our algorithm can shorten job execution time greatly and reduce bandwidth consumption compared to its non-adaptive counterpart which outperforms other existing replica placement methods.