English

MIND: In-Network Memory Management for Disaggregated Data Centers

Distributed, Parallel, and Cluster Computing 2021-10-22 v2

Abstract

Memory-compute disaggregation promises transparent elasticity, high utilization and balanced usage for resources in data centers by physically separating memory and compute into network-attached resource "blades". However, existing designs achieve performance at the cost of resource elasticity, restricting memory sharing to a single compute blade to avoid costly memory coherence traffic over the network. In this work, we show that emerging programmable network switches can enable an efficient shared memory abstraction for disaggregated architectures by placing memory management logic in the network fabric. We find that centralizing memory management in the network permits bandwidth and latency-efficient realization of in-network cache coherence protocols, while programmable switch ASICs support other memory management logic at line-rate. We realize these insights into MIND, an in-network memory management unit for rack-scale memory disaggregation. MIND enables transparent resource elasticity while matching the performance of prior memory disaggregation proposals for real-world workloads.

Keywords

Cite

@article{arxiv.2107.00164,
  title  = {MIND: In-Network Memory Management for Disaggregated Data Centers},
  author = {Seung-seob Lee and Yanpeng Yu and Yupeng Tang and Anurag Khandelwal and Lin Zhong and Abhishek Bhattacharjee},
  journal= {arXiv preprint arXiv:2107.00164},
  year   = {2021}
}

Comments

18 pages, 9 figures, 2 tables

R2 v1 2026-06-24T03:47:18.130Z