In 1987, Jim Gray and Gianfranco Putzolu introduced the five-minute rule, a simple, storage-memory-economics-based heuristic for deciding when data should live in DRAM rather than on storage. Subsequent revisits to the rule largely retained that economics-only view, leaving host costs, feasibility limits, and workload behavior out of scope. This paper revisits the rule from first principles, integrating host costs, DRAM bandwidth/capacity, and physics-grounded models of SSD performance and cost, and then embedding these elements in a constraint- and workload-aware framework that yields actionable provisioning guidance. We show that, for modern AI platforms, especially GPU-centric hosts paired with ultra-high-IOPS SSDs engineered for fine-grained random access, the DRAM$\leftrightarrow$flash caching threshold collapses from minutes to a few seconds. This shift reframes NAND flash memory as an \emph{active data tier} and exposes a broad research space across the hardware-software stack. We further introduce MQSim-Next, a calibrated SSD simulator that supports validation and sensitivity analysis and facilitates future architectural and system research. Finally, we present two concrete case studies that showcase the software system design space opened by such memory hierarchy paradigm shift. Overall, we turn a classical heuristic into an actionable, feasibility-aware analysis and provisioning framework and set the stage for further research on AI-era memory hierarchy.

翻译：1987年，吉姆·格雷与吉安弗兰科·普佐卢提出了"五分钟法则"——一种基于存储-内存经济学的简易启发式规则，用于判断数据应驻留于DRAM还是存储设备。后续对该规则的修订基本保留了其纯经济学视角，未考虑主机成本、可行性限制及工作负载行为。本文从第一性原理出发重新审视该规则，整合了主机成本、DRAM带宽/容量以及基于物理学的SSD性能与成本模型，并将这些要素嵌入一个约束感知与工作负载感知的框架中，从而生成可行的容量配置指导。研究表明，对于现代AI平台——尤其是搭配为细粒度随机访问而优化的超高速IOPS SSD的GPU密集型主机——DRAM与闪存间的缓存阈值从分钟级骤降至数秒级。这一转变将NAND闪存重新定义为"活跃数据层级"，并在软硬件全栈中开辟了广阔的研究空间。我们进一步介绍了MQSim-Next——一款经校准的SSD模拟器，支持验证分析与敏感性分析，有助于推进未来架构与系统研究。最后，通过两个具体案例研究展示此类内存层级范式转换所开启的软件系统设计空间。总体而言，我们将经典启发式方法转化为具备可行性分析与感知能力的配置框架，为AI时代内存层级的后续研究奠定基础。