ArtMem: Adaptive Migration in Reinforcement Learning-Enabled Tiered Memory
Yi, Xinyue Du, Hongchao Wang, Yu Zhang, Jie Li, Qiao Xue, Chun Jason
Yi, Xinyue
Du, Hongchao
Wang, Yu
Zhang, Jie
Li, Qiao
Xue, Chun Jason
Supervisor
Department
Computer Science
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Abstract
With the increasing memory demands of emerging applications, tiered memory has become a viable solution for reducing data center hardware costs. Given the low performance of the capacity tiers in tiered memory systems, optimizing memory management is crucial in improving overall system performance. This paper identifies three key limitations in existing tiered memory solutions. First, existing solutions often perform differently across different workloads, leading to suboptimal performance in some workloads. Second, they often fail to adjust migration strategies in response to low fast memory tier access rates, resulting in ineffective data placement. Third, they often miss the opportunity to dynamically tune the memory migration scope based on workload patterns, leading to unnecessary page migrations and under-utilization of tiered memory potential. This paper proposes ArtMem, a reinforcement learning (RL)-driven framework that dynamically manages tiered memory systems and adapts to workload evolution to address these limitations. ArtMem enables better placement of memory pages, enhancing system performance while reducing unnecessary migrations. Experimental evaluations show that ArtMem outperforms state-of-the-art tiering systems, achieving 35% - 172% performance improvements over diverse workloads.
Citation
X. Yi, H. Du, Y. Wang, J. Zhang, Q. Li, and C. J. Xue, “ArtMem: Adaptive Migration in Reinforcement Learning-Enabled Tiered Memory,” Proceedings of the 52nd Annual International Symposium on Computer Architecture, pp. 405–418, Jun. 2025, doi: 10.1145/3695053.3731001
Source
Proceedings of the 52nd Annual International Symposium on Computer
Conference
ISCA '25: 52nd Annual International Symposium on Computer Architecture
Keywords
Operating system, Tiered memory management, Virtual memory, Reinforcement learning
Subjects
Source
ISCA '25: 52nd Annual International Symposium on Computer Architecture
Publisher
Association for Computing Machinery