用大模型理解应用意图，自动优化存储系统性能。
Intent-Driven Storage Systems: From Low-Level Tuning to High-Level Understanding
- 通过大模型解析非结构化信号，推断工作负载语义意图
- 在FileBench上将IOPS提升最高2.45倍，支持跨层参数调优
- 适合需要自适应、智能化存储优化的研发与运维人员
现有存储系统缺乏对工作负载意图的可见性，难以适应现代大规模数据密集型应用的语义需求，导致依赖脆弱的启发式策略和碎片化的局部优化。为此，我们提出意图驱动的存储系统（IDSS），一种新范式：利用大语言模型（LLMs）从非结构化信号中推断工作负载与系统意图，指导跨层参数动态重配置。IDSS提供对竞争需求的整体推理，在策略约束下生成安全高效的决策。我们提出四个将LLM融入存储控制环的设计原则，并构建相应系统架构。在FileBench工作负载上的初步结果表明，通过理解意图并生成可执行的缓存、预取等配置，IDSS可使IOPS最高提升2.45倍。这表明，在约束条件下嵌入结构化流程中，LLM可作为高层语义优化器，弥合应用目标与底层控制之间的鸿沟。IDSS指向一个更具自适应性、自主性和动态响应能力的未来存储系统。
原文摘要 · Abstract (English)
Existing storage systems lack visibility into workload intent, limiting their ability to adapt to the semantics of modern, large-scale data-intensive applications. This disconnect leads to brittle heuristics and fragmented, siloed optimizations. To address these limitations, we propose Intent-Driven Storage Systems (IDSS), a vision for a new paradigm where large language models (LLMs) infer workload and system intent from unstructured signals to guide adaptive and cross-layer parameter reconfiguration. IDSS provides holistic reasoning for competing demands, synthesizing safe and efficient decisions within policy guardrails. We present four design principles for integrating LLMs into storage control loops and propose a corresponding system architecture. Initial results on FileBench workloads show that IDSS can improve IOPS by up to 2.45X by interpreting intent and generating actionable configurations for storage components such as caching and prefetching. These findings suggest that, when constrained by guardrails and embedded within structured workflows, LLMs can function as high-level semantic optimizers, bridging the gap between application goals and low-level system control. IDSS points toward a future in which storage systems are increasingly adaptive, autonomous, and aligned with dynamic workload demands.
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