# Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning

arXiv: https://arxiv.org/abs/2403.18998 · HTML: https://mlsanity.com/paper/2403.18998/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Task-Agnostic Meta-Learning for Few-shot Learning](https://mlsanity.com/paper/1805.07722/) (2018)
- [Log-based Anomaly Detection Without Log Parsing](https://mlsanity.com/paper/2108.01955/) (2021)
- [TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning](https://mlsanity.com/paper/1912.09033/) (2019)
- [How to Train Your MAML to Excel in Few-Shot Classification](https://mlsanity.com/paper/2106.16245/) (2021)
- [Training Medical Image Analysis Systems like Radiologists](https://mlsanity.com/paper/1805.10884/) (2018)
- [Few-shot Metric Learning: Online Adaptation of Embedding for Retrieval](https://mlsanity.com/paper/2211.07116/) (2022)
- [OneLog: Towards End-to-End Training in Software Log Anomaly Detection](https://mlsanity.com/paper/2104.07324/) (2021)
- [LogLead -- Fast and Integrated Log Loader, Enhancer, and Anomaly Detector](https://mlsanity.com/paper/2311.11809/) (2023)

## Built on by (most cited first, 2 of 2)

- [OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning](https://mlsanity.com/paper/2605.02906/) (2026)
- [LATS-RCA: Language Agent Tree Search for Root Cause Analysis in Microservices](https://mlsanity.com/paper/2605.03505/) (2026)

## Abstract (arXiv)

Microservice-based systems (MSS) may fail with various fault types. While existing AIOps methods excel at detecting abnormal traces and locating the responsible service(s), human efforts are still required for diagnosing specific fault types and failure causes.This paper presents TraFaultDia, a novel AIOps framework to automatically classify abnormal traces into fault categories for MSS. We treat the classification process as a series of multi-class classification tasks, where each task represents an attempt to classify abnormal traces into specific fault categories for a MSS. TraFaultDia leverages meta-learning to train on several abnormal trace classification tasks with a few labeled instances from a MSS, enabling quick adaptation to new, unseen abnormal trace classification tasks with a few labeled instances across MSS. TraFaultDia's use cases are scalable depending on how fault categories are built from anomalies within MSS. We evaluated TraFaultDia on two MSS, TrainTicket and OnlineBoutique, with open datasets where each fault category is linked to faulty system components (service/pod) and a root cause. TraFaultDia automatically classifies abnormal traces into these fault categories, thus enabling the automatic identification of faulty system components and root causes without manual analysis. TraFaultDia achieves 93.26% and 85.20% accuracy on 50 new classification tasks for TrainTicket and OnlineBoutique, respectively, when trained within the same MSS with 10 labeled instances per category. In the cross-system context, when TraFaultDia is applied to a MSS different from the one it is trained on, TraFaultDia gets an average accuracy of 92.19% and 84.77% for the same set of 50 new, unseen abnormal trace classification tasks of the respective systems, also with 10 labeled instances provided for each fault category per task in each system.