MIND: In-Network Memory Management for Disaggregated Data Centers

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 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.