SLIM: a Scalable Light-weight Root Cause Analysis for Imbalanced Data in Microservice

TL;DR

This paper introduces SLIM, a scalable and lightweight fault localization method tailored for imbalanced data in microservice change services, improving accuracy and interpretability with low training overhead.

Contribution

SLIM is a novel decision rule set-based approach that effectively handles imbalanced fault data and offers interpretable results, outperforming existing methods in accuracy and efficiency.

Findings

Outperforms existing fault localization algorithms in accuracy

Provides interpretable fault causes for easier verification

Operates with only 15% training overhead compared to SOTA methods

Abstract

The newly deployed service -- one kind of change service, could lead to a new type of minority fault. Existing state-of-the-art 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 showcase that our algorithm outperforms existing fault localization algorithms in both accuracy and model interpretability.