MIND: In-Network Memory Management for Disaggregated Data Centers
Seung-seob Lee, Yanpeng Yu, Yupeng Tang, Anurag Khandelwal, Lin Zhong,, and Abhishek Bhattacharjee

TL;DR
MIND leverages programmable network switches to enable efficient, elastic, and coherent shared memory in disaggregated data centers, improving resource utilization without sacrificing performance.
Contribution
The paper introduces MIND, a novel in-network memory management system that uses programmable switches to achieve elastic and coherent memory sharing in disaggregated architectures.
Findings
MIND achieves performance comparable to existing disaggregation methods.
In-network cache coherence protocols are bandwidth and latency efficient.
Programmable switches support complex memory management logic at line-rate.
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…
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