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P2 Thesios: Synthesizing Accurate Counterfactual I/O Traces from I/O Samples

Thesios:从 I/O 采样合成精确的反事实 I/O 轨迹
ASPLOS Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 32024匹配度 1/10 · 该论文研究分布式存储系统中 I/O 轨迹采样与反事实合成,属于存储系统/性能建模方向,与用户关注的 PL/AI、静态分析、Agentic 程序、浮点程序分析及贝叶斯程序分析无直接交集。引用 14 inbox
Phitchaya Mangpo Phothilimthana, Saurabh Kadekodi, Soroush Ghodrati, Selene Moon, Martin Maas
crossref: 2026-09-12dblp: 2026-09-12

AI 速览

一句话:提出 Thesios,融合多磁盘降采样 I/O 轨迹,合成高精度反事实全分辨率 I/O 轨迹。

大规模分布式存储系统设计需要代表性的 I/O 活动模型,尤其需要反事实“what-if”分析来评估新存储策略或硬件,但生产环境 A/B 实验成本高且有风险。Thesios 通过组合来自多台存储服务器、多块磁盘的降采样 I/O 轨迹,合成假设场景下的全分辨率 I/O 轨迹。作者利用 Google 已有常规采样轨迹进行评估,合成轨迹在读写请求数上达到 95–99.5% 准确率,利用率 90–97%,读延迟 80–99.8%。论文通过四个案例研究展示其能力:改变磁盘利用率、满度和容量,评估新数据放置策略,分析降低 RPM 对功耗与性能的影响,以及理解增大缓冲区缓存对存储服务器的影响。

问题
设计大规模分布式存储系统时,缺少经济、安全的反事实分析手段来评估假设的新存储策略或硬件变更。
方法
从多台存储服务器上多块磁盘收集降采样 I/O 轨迹,并仔细组合合成目标场景下的全分辨率 I/O 轨迹。
评估
与真实磁盘采集的指标对比准确率,并通过四个案例研究验证反事实 I/O 轨迹合成与假设策略、硬件及服务器变更分析。
原文摘要(Abstract)

Representative modeling of I/O activity is crucial when designing large-scale distributed storage systems. Particularly important use cases are counterfactual "what-if" analyses that assess the impact of anticipated or hypothetical new storage policies or hardware prior to deployment. We propose Thesios, a methodology to accurately synthesize such hypothetical full-resolution I/O traces by carefully combining down-sampled I/O traces collected from multiple disks attached to multiple storage servers. Applying this approach to real-world traces that are already routinely sampled at Google, we show that our synthesized traces achieve 95--99.5% accuracy in read/write request numbers, 90--97% accuracy in utilization, and 80--99.8% accuracy in read latency compared to metrics collected from actual disks. We demonstrate how Thesios enables diverse counterfactual I/O trace synthesis and analyses of hypothetical policy, hardware, and server changes through four case studies: (1) studying the effects of changing disk's utilization, fullness, and capacity, (2) evaluating new data placement policy, (3) analyzing the impact on power and performance of deploying disks with reduced rotations-per-minute (RPM), and (4) understanding the impact of increased buffer cache size on a storage server. Without Thesios, such counterfactual analyses would require costly and potentially risky A/B experiments in production.

链接与引用

DOI 原文 · DBLP

BibTeX
@inproceedings{PhothilimthanaK24,
  title = {Thesios: Synthesizing Accurate Counterfactual I/O Traces from I/O Samples},
  author = {Phitchaya Mangpo Phothilimthana and Saurabh Kadekodi and Soroush Ghodrati and Selene Moon and Martin Maas},
  booktitle = {ASPLOS},
  year = {2024},
  doi = {10.1145/3620666.3651337}
}