MemFlex: A Hybrid Memory System to Boost Cost of Ownership in Data Centers

Chandranil Chakraborttii, Mohammad Maminur Islam

Abstract: Modern large-scale computing clusters face scalability challenges with traditional DRAM-based memory systems due to issues like increasing cell leakage current and reduced reliability. To overcome these limitations, alternative memory solutions have emerged, including 3D-stacked DRAM and emerging non-volatile memory (NVM) technologies. However, these alternatives are unlikely to fully replace DRAM due to capacity constraints and higher cost-per-bit. Hybrid memory systems, combining DRAM with NVM technologies, offer a cost-effective solution by leveraging the strengths of both memory types. Effective data placement decisions are crucial for optimizing hybrid memory systems. This paper introduces MemFlex, an machine learning (ML)-driven approach for migrating pages between memory tiers in a hybrid memory system based on predicted page lifetimes. MemFlex utilizes application-specific ML models to predict death-time ranges with high accuracy, guiding placement decisions to the appropriate storage tier. Evaluation results demonstrate MemFlex's superiority over state-of-the-art techniques, achieving an average performance improvement of 19% over evaluated baselines, with minimal performance degradation. This paper contributes to hybrid memory management research, leveraging real-world traces for evaluation, and introducing an ML based approach for optimized data placement decisions.

External IDs:dblp:conf/compsac/ChakraborttiiI24