一句话：用决策规则集在基数约束下优化 F1，为变更服务中的不平衡故障提供可解释、低开销的故障定位。
真实微服务系统中，新部署的服务（一种变更服务）可能引发新类型的少数类故障，而现有 SOTA 故障定位方法很少考虑变更服务场景下的故障类别不平衡问题。本文提出 SLIM：以决策规则集为模型，在基数约束下直接优化 F1 分数以应对高度不平衡数据；算法贪心地生成边际增益最大的规则，并用高效的 minorize-maximization（MM）方法迭代选择规则，从而最大化一个非单调次模下界。相比已有故障定位算法，SLIM 能适应变更服务的不平衡故障场景，并给出易于理解和验证的可解释故障原因；同时可部署于在线训练设置，训练开销仅为当前 SOTA 方法的约 15%。实证研究表明，该算法在准确率与模型可解释性两方面均优于现有故障定位算法。
In real-world microservice systems, the newly deployed service - one kind of change service, could lead to a new type of minority fault. Existing state-of-the-art (SOTA) methods for fault localization rarely consider the imbalanced fault classification in change service. This paper proposes a novel method that utilizes decision rule sets to deal with highly imbalanced data by optimizing the F1 score subject to cardinality constraints. The proposed method greedily generates the rule with maximal marginal gain and uses an efficient minorize-maximization (MM) approach to select rules iteratively, maximizing a non-monotone submodular lower bound. Compared with existing fault localization algorithms, our algorithm can adapt to the imbalanced fault scenario of change service, and provide interpretable fault causes which are easy to understand and verify. Our method can also be deployed in the online training setting, with only about 15% training overhead compared to the current SOTA methods. Empirical studies demonstrate the superior performance of our algorithm to existing fault localization algorithms in terms of both accuracy and model interpretability.
DOI 原文 ·
@inproceedings{paperbot8291,
title = {SLIM: a Scalable and Interpretable Light-weight Fault Localization Algorithm for Imbalanced Data in Microservice},
author = {Rui Ren and Jingbang Yang and Linxiao Yang and Xinyue Gu and Liang Sun},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3694984}
}