PatternS: An intelligent hybrid memory scheduler driven by page pattern recognition

Abstract

Hybrid memory systems integrate a variety of memory technologies, effectively expanding the main memory capacity to meet the demands of emerging big data applications. Hybrid memory systems exhibit disparities in their heterogeneous memory components’ access speeds. Dynamic page scheduling to ensure memory access predominantly occurs in the faster memory components is essential for optimizing the performance of hybrid memory systems. Traditional history schedulers are unable to predict irregular memory accesses. Therefore, recent works attempt to optimize page scheduling by predicting their hotness using neural network models. However, they face two crucial challenges: one is the page explosion problem caused by the massive number of pages and the other is the new pages problem due to shifting memory access regions over time. To address these two challenges, we propose PatternS, an intelligent hybrid memory scheduler driven by page pattern recognition. Based on the insight into the similarities between memory access patterns, we proposed a Page Pattern Recognizer to identify pages with similar patterns and manage them as groups. PatternS is also capable of categorizing new pages into pre-identified patterns using short-term access information, enabling them to be predicted by the trained model. Experimental results demonstrate that our approach outperforms state-of-the-art intelligent schedulers regarding effectiveness and cost.