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Database replication and query load-balancing are mechanisms to scale query throughput. The analysis of workloads allows loadbalancing queries to replica nodes according to their accessed data. As a result, replica nodes must only store and synchronize subsets of the data. However, balancing the load of large-scale workloads evenly while minimizing the memory footprint is complex and challenging. State-of-the-art allocation approaches are either time consuming or the resulting allocations are not memory-efficient. Further, partial replication approaches usually optimize only against a single fixed workload. If the actual workload deviates from this expected one, load-balancing can be highly skewed, resulting in severe performance degradation. This paper proposes a novel approach to compute memoryefficient fragment allocations that enable balancing multiple potential future workloads. Applied on the TPS-DS benchmark and a large-size enterprise workload, we show that, compared to state-of-the-art allocations, our solutions are (i) more flexible, (ii) require up to 50% less data, (iii) have competitive runtimes, and (iv) are more robust against uncertain out-of-sample workloads.

Robust and Memory-Efficient Database Fragment Allocation for Large and Uncertain Database Workloads