Intelligent Page Migration on Heterogeneous Memory by Using Transformer

Abstract

Locality-based migration strategies are widely used in existing memory space management. Such type of strategies are consistently confronts with challenges in efficiently managing pages migration within constrained memory space, especially when new architecture such as hybrid of DRAM and NVM are emerging. Here we propose TransMigrator, an innovative predictive page migration model based on transformer architecture, which obtains a qualitative leap in the breadth and accuracy of prediction compared with traditional local-based methods. TransMigrator utilizes an end-to-end neural network to learn memory access behavior and page migration record in the long-term history and predict the most likely next page to fetch. Furthermore, a migration-management mechanism is designed to support the page-feeding from predictor, which in another way enhance the model robustness. The model achieves an average prediction accuracy better than 0.72, and saves an average of 0.24 access time overhead compared to strategies such as AC-CLOCK, THMigrator, and VC-HMM.