一句话：提出 Mint，将 trace 从“1 或 0”采样转为“共性+可变性”范式，在 agent 端实现全请求追踪并大幅降低存储与网络开销。
分布式追踪数据量巨大，框架设计需在保留关键 trace 信息与降低数据量之间权衡。已有方法多采用“1 或 0”采样，即保留采样 trace、丢弃未采样 trace，但作者基于真实生产 trace 的实证研究发现该策略难以有效平衡。Mint 将范式转为“共性+可变性”：先将 trace 解析为公共模式与可变参数，再对模式进行聚合、对参数进行过滤，并在 agent 端实现全请求捕获。实验表明，Mint 能捕获所有 trace，同时将存储降至平均 2.7%、网络开销降至平均 4.2%，且足够轻量以用于生产环境。
Distributed traces contain valuable information but are often massive in volume, posing a core challenge in tracing framework design: balancing the tradeoff between preserving essential trace information and reducing trace volume. To address this tradeoff, previous approaches typically used a '1 or 0' sampling strategy: retaining sampled traces while completely discarding unsampled ones. However, based on an empirical study on real-world production traces, we discover that the '1 or 0' strategy actually fails to effectively balance this tradeoff. To achieve a more balanced outcome, we shift the strategy from the '1 or 0' paradigm to the 'commonality + variability' paradigm. The core of 'commonality + variability' paradigm is to first parse traces into common patterns and variable parameters, then aggregate the patterns and filter the parameters. We propose a cost-efficient tracing framework, Mint, which implements the 'commonality + variability' paradigm on the agent side to enable all requests capturing. Our experiments show that Mint can capture all traces and retain more trace information while optimizing trace storage (reduced to an average of 2.7%) and network overhead (reduced to an average of 4.2%). Moreover, experiments also demonstrate that Mint is lightweight enough for production use.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2411-04605,
title = {Mint: Cost-Efficient Tracing with All Requests Collection via Commonality and Variability Analysis},
author = {Haiyu Huang and Cheng Chen and Kunyi Chen and Pengfei Chen and Guangba Yu and Zilong He and Yilun Wang and Huxing Zhang and Qi Zhou},
booktitle = {ASPLOS},
year = {2025},
doi = {10.1145/3669940.3707287}
}