2026/04/14 by Xi Wang, Jie Liu, Shuangyan Yang +3 · 1 voice
Computer Science · #Advanced Data Storage Technologies #Cloud Computing and Resource Management #Extended memory #Flat memory model #Memory leak #Memory management #Memory map #Parallel Computing and Optimization Techniques #Performance tuning #Reinforcement learning #Software #cs.OS
paper · pdf · doi:10.48550/arxiv.2604.12165
openalex publication_date 2026/04/14 · arxiv published 2026/04/14 · arxiv updated 2026/04/14 · openalex created_date 2026/04/16 · openalex updated_date 2026/07/28
Memory tiering provides a cost-effective solution to increase memory capacity, utilization, and even bandwidth. Memory tiering relies on system software for memory profiling, detection of frequently accessed pages, and page migration. Such a system software often comes with system parameters. The configurations of those parameters impact application performance. We comprehensively classify system parameters, and characterize the sensitivity of application performance to them using representative memory tiering solutions. Furthermore, we introduce a lightweight and user-friendly framework PTMT, which automates tuning of parameters at runtime for various memory tiering solutions. We identify major challenges for online tuning of memory tiering. PTMT uses a hybrid "offline + online" tuning method: while the offline phase builds a performance database for online queries and reduces runtime overhead, the online phase uses reinforcement learning (customized to memory tiering) to tune. PTMT improves performance by 30%, 26%, 21%, and 14%, on four memory tiering solutions (TPP, UPM, Colloid, and AutoNUMA), compared to using the default configurations. PTMT outperforms the state-of-the-art by 32% on average.