Exploration of Dynamic Query-Based Load Balancing for Partially Replicated Database Systems with Node Failures

Abstract

Database replication is a mechanism to achieve scalability, for example, by executing queries independently on replica nodes. Partial replication is an approach to minimize the overall memory consumption of a replication cluster while still enabling a balanced load distribution among nodes to scale the query throughput linearly with the number of replicas. Partial replication reduces the cluster costs, speeds up data synchronization, and improves caching. However, load balancing may become skewed in the case of unexpected query distributions, unfavorable query timings, or node failures. To simulate and visualize the load balancing behavior for specific data fragment allocations, we implemented an interactive application. It allows users to retrace and evaluate the end-to-end performance of partially replicated database systems in varying experiments. Using our tool, we find that existing allocation approaches are either not memory-efficient or may result in load imbalances when nodes fail. We show that our novel robust allocation strategy achieves a better workload distribution with even less memory.