research-article

Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-modal Observability Data

Authors:

Zibin ZhengAuthors Info & Claims

ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 553 - 565

https://doi.org/10.1145/3611643.3616249

Published: 30 November 2023 Publication History

ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-modal Observability Data

Pages 553 - 565

Abstract
References

Abstract

Root cause analysis (RCA) in large-scale microservice systems is a critical and challenging task. To understand and localize root causes of unexpected faults, modern observability tools collect and preserve multi-modal observability data, including metrics, traces, and logs. Since system faults may manifest as anomalies in different data sources, existing RCA approaches that rely on single-modal data are constrained in the granularity and interpretability of root causes. In this study, we present Nezha, an interpretable and fine-grained RCA approach that pinpoints root causes at the code region and resource type level by incorporative analysis of multi-modal data. Nezha transforms heterogeneous multi-modal data into a homogeneous event representation and extracts event patterns by constructing and mining event graphs. The core idea of Nezha is to compare event patterns in the fault-free phase with those in the fault-suffering phase to localize root causes in an interpretable way. Practical implementation and experimental evaluations on two microservice applications show that Nezha achieves a high top1 accuracy (89.77%) on average at the code region and resource type level and outperforms state-of-the-art approaches by a large margin. Two ablation studies further confirm the contributions of incorporating multi-modal data.

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Cited By

Gu WLiu JChen ZZhang JSu YGu JFeng CYang ZYang YLyu M(2024)Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal FeaturesACM Transactions on Software Engineering and Methodology10.1145/370297834:3(1-31)Online publication date: 5-Nov-2024
https://dl.acm.org/doi/10.1145/3702978
Sun YLin ZShi BZhang SMa SJin PZhong ZPan LGuo YPei D(2024)Interpretable Failure Localization for Microservice Systems Based on Graph AutoencoderACM Transactions on Software Engineering and Methodology10.1145/369599934:2(1-28)Online publication date: 13-Sep-2024
https://dl.acm.org/doi/10.1145/3695999
Sun YShi BMao MMa MXia SZhang SPei DFilkov VRay BZhou M(2024)ART: A Unified Unsupervised Framework for Incident Management in Microservice SystemsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695495(1183-1194)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695495
Show More Cited By

Index Terms

Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-modal Observability Data
1. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software performance
      2. Software reliability
    2. Software system structures
      1. Distributed systems organizing principles
        Cloud computing

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cover image ACM Conferences

ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

November 2023

2215 pages

ISBN:9798400703270

DOI:10.1145/3611643

General Chair:
Satish Chandra
Google, USA
,
Program Chairs:
Kelly Blincoe
University of Auckland, New Zealand
,
Paolo Tonella
USI Lugano, Switzerland

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Published: 30 November 2023

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ESEC/FSE '23: 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

December 3 - 9, 2023

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Cited By

Gu WLiu JChen ZZhang JSu YGu JFeng CYang ZYang YLyu M(2024)Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal FeaturesACM Transactions on Software Engineering and Methodology10.1145/370297834:3(1-31)Online publication date: 5-Nov-2024
https://dl.acm.org/doi/10.1145/3702978
Sun YLin ZShi BZhang SMa SJin PZhong ZPan LGuo YPei D(2024)Interpretable Failure Localization for Microservice Systems Based on Graph AutoencoderACM Transactions on Software Engineering and Methodology10.1145/369599934:2(1-28)Online publication date: 13-Sep-2024
https://dl.acm.org/doi/10.1145/3695999
Sun YShi BMao MMa MXia SZhang SPei DFilkov VRay BZhou M(2024)ART: A Unified Unsupervised Framework for Incident Management in Microservice SystemsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695495(1183-1194)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695495
Tao LZhang SJia ZSun JMa MLi ZSun YYang CZhang YPei DFilkov VRay BZhou M(2024)Giving Every Modality a Voice in Microservice Failure Diagnosis via Multimodal Adaptive OptimizationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695489(1107-1119)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695489
Zeng HChen NDeng T(2024)LSTD-MTS: Anomaly Detection with Capturing Long-Term Spatio-Temporal Dependence for Multi-dimensional Time SeriesProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3671383(397-406)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3671383
Aktaş KKilinc H(2024)Interaction Prediction and Anomaly Detection in a Microservices-based Telecommunication PlatformProceedings of the 2024 International Conference on Software and Systems Processes10.1145/3666015.3666017(56-65)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3666015.3666017
Yao ZPei CChen WWang HSu LJiang HXie ZNie XPei Dd'Amorim M(2024)Chain-of-Event: Interpretable Root Cause Analysis for Microservices through Automatically Learning Weighted Event Causal GraphCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663827(50-61)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663529.3663827
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