Adaptive Data Replication Optimization Based on Reinforcement Learning

Data replication plays an important role in enterprise IT landscapes, where data is shared among multiple IT systems. IT administrators need to tune the replicating software's configuration setting for it to perform at its optimum level. It is a challenge to continue optimizing the software's configuration to keep up with the fluctuating workload in a dynamic business environment. We propose a novel approach of using reinforcement learning with meta-heuristics to create an adaptive optimization method for data replication software. The experimental results show the replicating software managed by the proposed approach can perform at an optimum level despite consistently working under changing workloads.