一句话：提出模态无关框架 Medicine，通过分模态编码与自适应优化平衡各模态学习节奏，提升微服务故障诊断。
微服务系统复杂且易发生故障，而仅依赖日志、指标或追踪等单模态数据的诊断难以全面刻画故障模式；现有多模态方法又常被主导模态压制，使低贡献模态难以发挥作用。本文提出模态无关的微服务故障诊断框架 Medicine，对不同模态分别编码以保留其独有特征，并采用多模态自适应优化调整各模态间的学习节奏，从而提升整体诊断性能。在三个公开数据集上，Medicine 优于现有单模态与多模态诊断方法，F1 分数提升 15.72% 至 70.84%；在部分模态数据缺失或质量较低时，仍能保持较高诊断准确率。
Microservice systems are inherently complex and prone to failures, which can significantly impact user experience. Existing diagnostic approaches based on single-modal data such as logs, metrics, or traces cannot comprehensively capture failure patterns. For those multimodal data-based failure diagnosis methods, the dominant modality can overshadow others, hindering low-yield modalities from fully leveraging their characteristics. This paper proposes Medicine, a modal-independent microservice failure diagnosis framework based on multimodal adaptive optimization. It encodes different modalities separately to retain their unique features and employs adaptive optimization to adjust the learning pace between modalities, thereby enhancing overall diagnostic performance. Experimental results demonstrate that Medicine outperforms existing single-modal and multimodal diagnostic approaches on three public datasets, with F1-score improving by 15.72% to 70.84%. Even in cases where individual modal data is missing or of lower quality, Medicine maintains high diagnostic accuracy.
DOI 原文 ·
@inproceedings{paperbot8337,
title = {Giving Every Modality a Voice in Microservice Failure Diagnosis via Multimodal Adaptive Optimization},
author = {Lei Tao and Shenglin Zhang and Zedong Jia and Jinrui Sun and Minghua Ma and Zhengdan Li and Yongqian Sun and Canqun Yang and Yuzhi Zhang and Dan Pei},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695489}
}