Microservice Root Cause Analysis With Limited Observability Through Intervention Recognition in the Latent Space-Reference-Cited by-同舟云学术

Microservice Root Cause Analysis With Limited Observability Through Intervention Recognition in the Latent Space

Published:2024-08-24 Issue: Volume: Page:6049-6060
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Xie Zhe¹^ORCID,Zhang Shenglin²^ORCID,Geng Yitong³^ORCID,Zhang Yao³^ORCID,Ma Minghua⁴^ORCID,Nie Xiaohui⁵^ORCID,Yao Zhenhe¹^ORCID,Xu Longlong¹^ORCID,Sun Yongqian²^ORCID,Li Wentao³^ORCID,Pei Dan¹^ORCID

Affiliation:

1. BNRist, Tsinghua University, Beijing, China

2. Nankai University, Tianjin, China

3. eBay Inc., Shanghai, China

4. Microsoft, Redmond, USA

5. Computer Network Information Center, Chinese Academy of Sciences, Beijing, China

Funder

Beijing National Research Center for Information Science and Technology (BNRist)

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671530

Reference26 articles.

1. Pooja Aggarwal Ajay Gupta Prateeti Mohapatra Seema Nagar Atri Mandal Qing Wang and Amit Paradkar. 2021. Localization of Operational Faults in Cloud Applications by Mining Causal Dependencies in Logs Using Golden Signals. In Service-Oriented Computing textendash ICSOC 2020 Workshops Hakim Hacid Fatma Outay Hye-young Paik Amira Alloum Marinella Petrocchi Mohamed Reda Bouadjenek Amin Beheshti Xumin Liu and Abderrahmane Maaradji (Eds.). Vol. 12632. Springer International Publishing Cham 137--149. https://doi.org/10.1007/978-3-030-76352-7_17

2. CauseInfer: Automatic and distributed performance diagnosis with hierarchical causality graph in large distributed systems

3. Research on Architecting Microservices: Trends, Focus, and Potential for Industrial Adoption

4. Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, and Michele Sebag. 2018. Learning functional causal models with generative neural networks. Explainable and interpretable models in computer vision and machine learning (2018), 39--80.

5. TrinityRCL: Multi-Granular and Code-Level Root Cause Localization Using Multiple Types of Telemetry Data in Microservice Systems