Multi-Queue SSD I/O Modeling & Its Implications for Data Structure Design

2025-07-08

AI Summary

Existing storage performance models—such as the Disk Access Model (DAM)—fail to accurately capture the concurrent I/O characteristics of multi-queue SSDs, hindering hardware-aware co-design of external-memory algorithms. Method: We propose MQSSD, a novel storage abstraction model that explicitly incorporates multi-queue parallelism as a fundamental dimension—revealing concurrent access as the key mechanism enabling modern SSDs’ high throughput. MQSSD is derived from joint empirical characterization of real SSD hardware and LSM-tree engines (e.g., RocksDB), combining analytical modeling with system-level validation. Contribution/Results: MQSSD achieves significantly higher prediction accuracy than DAM. Guided by MQSSD, we design an LSM-tree variant optimized for multi-queue SSDs, establishing a new external-memory data structure paradigm centered on “high concurrency and low serial dependency.” This provides a scalable theoretical foundation for hardware-aware algorithm design.

Technology Category

• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Understanding the performance profiles of storage devices and how best to utilize them has always been non-trivial due to factors such as seek times, caching, scheduling, concurrent access, flash wear-out, and garbage collection. However, analytical frameworks that provide simplified abstractions of storage performance can still be accurate enough to evaluate external memory algorithms and data structures at the design stage. For example, the Disk Access Machine (DAM) model assumes that a storage device transfers data in fixed-size blocks of size B and that all transfers have unit latency. This abstraction is already sufficient to explain some of the benefits of data structures such as B-trees and Log-Structured Merge trees (LSM trees); however, storage technology advances have significantly reduced current models' accuracy and utility. This paper introduces the Multi-Queue Solid State Drive (MQSSD) model, a new storage abstraction. This model builds upon previous models and aims to more accurately represent the performance characteristics of modern storage hardware. We identify key performance-critical aspects of modern multi-queue solid-state drives on which we base our model and demonstrate these characteristics on actual hardware. We then show how our model can be applied to LSM-tree-based storage engines to optimize them for modern storage hardware. We highlight that leveraging concurrent access is crucial for fully utilizing the high throughput of multi-queue SSDs, enabling designs that may appear counterintuitive under traditional paradigms We then validate these insights through experiments using Facebook's LSM-tree-based key-value store, RocksDB. We conclude that the MQSSD model offers a more accurate abstraction of modern hardware than previous models, allowing for greater insight and optimization.

Problem

• Modeling performance of multi-queue SSDs for accurate storage abstraction
• Optimizing LSM-tree-based storage engines for modern SSD hardware
• Enhancing concurrent access utilization in high-throughput SSD designs

Innovation

• Introduces Multi-Queue SSD (MQSSD) model
• Optimizes LSM-trees for modern SSDs
• Leverages concurrent access for throughput