Inter-data-center asynchronous middleware replication between active-active databases has become essential for achieving continuous business availability. Near real-time replication latency is expected despite intermittent peaks in transaction volumes. Database tables are divided for replication across multiple parallel replication consistency groups; each having a maximum throughput capacity, but doing so can break transaction integrity. It is often not known which tables can be updated by a common transaction. Independent replication also requires balancing resource utilization and latency objectives. Our work provides a method to optimize replication latencies, while minimizing transaction splits among a minimum of parallel replication consistency groups. We present a two-staged approach: a log-based workload discovery and analysis and a history-based database partitioning. The experimental results from a real banking batch workload and a benchmark OLTP workload demonstrate the effectiveness of our solution even for partitioning 1000s of database tables for very large workloads. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Min, H., Gao, Z., Li, X., Huang, J., Jin, Y., Bourbonnais, S., … Fuh, G. (2014). Inter-data-center large-scale database replication optimization - A workload driven partitioning approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8645 LNCS, pp. 417–432). Springer Verlag. https://doi.org/10.1007/978-3-319-10085-2_38
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