Optimizing the Data Migration Process Using Machine Learning Techniques

Efficient data migration is essential for organizations facing growing data volumes, evolving infrastructure, and network cost constraints. We focus on two common migration scenarios: high-speed cloud onboarding under tight downtime limits, and bandwidth-constrained synchronization over cost-sensitive links. While recent frameworks like DAMOCRO improve throughput and reduce cost using offline-trained classifiers to group similar data for compression, they suffer from the workload imbalance issue and the use of a fixed compression level. In this paper, we propose two complementary improvements to DAMOCRO: (1) a balanced clustering-based classifier that ensures even workload distribution across compression workers, which helps reduce bottlenecks and improve scalability; and (2) an adaptive compression-level predictor that adaptively selects compression settings based on data characteristics and network conditions to optimize end-to-end throughput. Experiments on five datasets demonstrate up to 43.6 % throughput improvement and up to 23.7 % cost reduction, confirming the effectiveness of the proposed enhancements in diverse industrial data migration settings.