Memory Disaggregation for AI Infrastructure

Authors

  • Cameron Reed Author

Keywords:

memory disaggregation, CXL, AI infrastructure, performance, resource pooling

Abstract

This systems review examines memory disaggregation for artificial-intelligence infrastructure. The organizing question is when pooled and remote memory improves utilization without unacceptable latency, interference, or recovery complexity. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to comparing capacity gains without modeling workload locality and failure domains. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in large-scale AI training and inference platforms.

References

  1. Aguilera, M. K., Amaro, E., Amit, N., Hunhoff, E., Yelam, A., & Zellweger, G. (2023). Memory disaggregation: why now and what are the challenges. ACM SIGOPS Operating Systems Review, 57(1), 38-46. https://doi.org/10.1145/3606557.3606563
  2. Heo, T., Kang, S., Lee, S., Hwang, S., & Huh, J. (2021). Hardware-assisted Trusted Memory Disaggregation for Secure Far Memory. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2108.11507
  3. Kwon, M., Jang, J., Choi, H., Lee, S., & Jung, M. (2023). Failure Tolerant Training With Persistent Memory Disaggregation Over CXL. IEEE Micro, 43(2), 66-75. https://doi.org/10.1109/mm.2023.3237548
  4. Maruf, H. A., & Chowdhury, M. (2023). Memory Disaggregation: Advances and Open Challenges. ACM SIGOPS Operating Systems Review, 57(1), 29-37. https://doi.org/10.1145/3606557.3606562
  5. Mehra, P., & Coughlin, T. (2022). Taming Memory With Disaggregation. Computer, 55(9), 94-98. https://doi.org/10.1109/mc.2022.3187847
  6. Quek, Y. T., Woo, W. L., & Logenthiran, T. (2019). Load Disaggregation Using One-Directional Convolutional Stacked Long Short-Term Memory Recurrent Neural Network. IEEE Systems Journal, 14(1), 1395-1404. https://doi.org/10.1109/jsyst.2019.2919668
  7. Wang, R., Wang, J., Idreos, S., Özsu, M. T., & Aref, W. G. (2022). The case for distributed shared-memory databases with RDMA-enabled memory disaggregation. Proceedings of the VLDB Endowment, 16(1), 15-22. https://doi.org/10.14778/3561261.3561263
  8. Wang, Z., Guo, Y., Lu, K., Wan, J., Wang, D., Yao, T., & Wu, H. (2024). Rcmp: Reconstructing RDMA-Based Memory Disaggregation via CXL. ACM Transactions on Architecture and Code Optimization, 21(1), 1-26. https://doi.org/10.1145/3634916
  9. Xia, M., Liu, W. A., Wang, K., Song, W., Chen, C., & Li, Y. (2020). Non-intrusive load disaggregation based on composite deep long short-term memory network. Expert Systems with Applications, 160, 113669. https://doi.org/10.1016/j.eswa.2020.113669
  10. Yu, X., Jia, X., Zhang, J., Wang, Y., Yu, S., & Qi, Z. (2025). Rethinking Virtual Machines Live Migration for Memory Disaggregation. IEEE Transactions on Parallel and Distributed Systems, 36(11), 2310-2324. https://doi.org/10.1109/tpds.2025.3597149

Downloads

Published

2026-08-08

Issue

Vol. 1 (2026): Archive

Section

Systems Review

License

Copyright (c) 2026 Computing Architectures and Data Systems

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.