ARMS: Adaptive and Robust Memory Tiering System

TL;DR

ARMS is a new memory tiering system that adaptively manages data placement across multiple memory tiers, achieving near-optimal performance without the need for manual threshold tuning, outperforming existing solutions.

Contribution

We introduce ARMS, an adaptive memory tiering system that eliminates the need for pre-configured thresholds through novel hot/cold identification and cost-based migration policies.

Findings

ARMS achieves within 3% of the best tuned performance.

ARMS outperforms prior systems by 1.26x to 2.3x without tuning.

Tuning improves performance by addressing hot/cold identification and migration efficiency.

Abstract

Memory tiering systems seek cost-effective memory scaling by adding multiple tiers of memory. For maximum performance, frequently accessed (hot) data must be placed close to the host in faster tiers and infrequently accessed (cold) data can be placed in farther slower memory tiers. Existing tiering solutions such as HeMem, Memtis, and TPP use rigid policies with pre-configured thresholds to make data placement and migration decisions. We perform a thorough evaluation of the threshold choices and show that there is no single set of thresholds that perform well for all workloads and configurations, and that tuning can provide substantial speedups. Our evaluation identified three primary reasons why tuning helps: better hot/cold page identification, reduced wasteful migrations, and more timely migrations. Based on this study, we designed ARMS - Adaptive and Robust Memory tiering System - to provide high performance without tunable thresholds. We develop a novel hot/cold page identification mechanism relying on short-term and long-term moving averages, an adaptive migration policy based on cost/benefit analysis, and a bandwidth-aware batched migration scheduler. Combined, these approaches provide out-of-the-box performance within 3% the best tuned performance of prior systems, and between 1.26x-2.3x better than prior systems without tuning.