标题： 多队列 SSD I/O 建模及其对数据结构设计的影响 显示英文标题

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

摘要： 理解存储设备的性能特征以及如何最佳利用它们一直具有挑战性，这是由于存在诸如寻道时间、缓存、调度、并发访问、闪存磨损和垃圾回收等因素。 然而，提供存储性能简化抽象的分析框架仍然足够准确，可以在设计阶段评估外部内存算法和数据结构。 例如，磁盘访问机（DAM）模型假设存储设备以固定大小为B的数据块传输数据，并且所有传输都具有单位延迟。 这种抽象已经足以解释某些数据结构（如B树和日志结构合并树（LSM树））的一些优势；然而，存储技术的进步已显著降低了当前模型的准确性与实用性。 本文介绍了多队列固态硬盘（MQSSD）模型，这是一种新的存储抽象。 该模型建立在之前模型的基础上，旨在更准确地表示现代存储硬件的性能特征。 我们确定了现代多队列固态硬盘的关键性能关键方面，并在实际硬件上展示了这些特性。 然后，我们展示了如何将我们的模型应用于基于LSM树的存储引擎，以优化它们以适应现代存储硬件。 我们强调，利用并发访问对于充分利用多队列SSD的高吞吐量至关重要，这使得一些在传统范式下可能显得反直觉的设计成为可能。 随后，我们通过使用Facebook基于LSM树的键值存储RocksDB进行实验来验证这些见解。 我们得出结论，MQSSD模型比之前的模型提供了更准确的现代硬件抽象，从而能够获得更大的洞察力和优化空间。 显示英文摘要

摘要： 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.