Robust Failure Diagnosis of Microservice System Through Multimodal Data
Sheng-Lin Zhang
1
,
Pengxiang Jin
1
,
Zihan Lin
1
,
Yong-qian Sun
1
,
Bicheng Zhang
2
,
Sibo Xia
1
,
Zhengdan Li
1
,
Zhenyu Zhong
1
,
Minghua Ma
3
,
Wa Jin
1
,
Dai Zhang
4
,
Zhenyu Zhu
4
,
Dan Pei
5
3
Microsoft, Beijing, China
|
4
ZhejiangE-CommerceBank Co., Ltd., Hangzhou, Zhejiang, China
|
Тип публикации: Journal Article
Дата публикации: 2023-11-01
scimago Q1
wos Q1
БС1
SJR: 1.441
CiteScore: 10.8
Impact factor: 5.8
ISSN: 19391374, 23720204
Computer Science Applications
Hardware and Architecture
Computer Networks and Communications
Information Systems and Management
Краткое описание
Automatic failure diagnosis is crucial for large microservice systems. Currently, most failure diagnosis methods rely solely on single-modal data (i.e., using either metrics, logs, or traces). In this study, we conduct an empirical study using real-world failure cases to show that combining these sources of data (multimodal data) leads to a more accurate diagnosis. However, effectively representing these data and addressing imbalanced failures remain challenging. To tackle these issues, we propose DiagFusion , a robust failure diagnosis approach that uses multimodal data. It leverages embedding techniques and data augmentation to represent the multimodal data of service instances, combines deployment data and traces to build a dependency graph, and uses a graph neural network to localize the root cause instance and determine the failure type. Our evaluations using real-world datasets show that DiagFusion outperforms existing methods in terms of root cause instance localization (improving by 20.9% to 368%) and failure type determination (improving by 11.0% to 169%).
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ГОСТ |
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MLA
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ГОСТ
Скопировать
Zhang S. et al. Robust Failure Diagnosis of Microservice System Through Multimodal Data // IEEE Transactions on Services Computing. 2023. Vol. 16. No. 6. pp. 3851-3864.
ГОСТ со всеми авторами (до 50)
Скопировать
Zhang S., Jin P., Lin Z., Sun Y., Zhang B., Xia S., Li Z., Zhong Z., Ma M., Jin W., Zhang D., Zhu Z., Pei D. Robust Failure Diagnosis of Microservice System Through Multimodal Data // IEEE Transactions on Services Computing. 2023. Vol. 16. No. 6. pp. 3851-3864.
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RIS
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TY - JOUR
DO - 10.1109/TSC.2023.3290018
UR - https://ieeexplore.ieee.org/document/10165686/
TI - Robust Failure Diagnosis of Microservice System Through Multimodal Data
T2 - IEEE Transactions on Services Computing
AU - Zhang, Sheng-Lin
AU - Jin, Pengxiang
AU - Lin, Zihan
AU - Sun, Yong-qian
AU - Zhang, Bicheng
AU - Xia, Sibo
AU - Li, Zhengdan
AU - Zhong, Zhenyu
AU - Ma, Minghua
AU - Jin, Wa
AU - Zhang, Dai
AU - Zhu, Zhenyu
AU - Pei, Dan
PY - 2023
DA - 2023/11/01
PB - Institute of Electrical and Electronics Engineers (IEEE)
SP - 3851-3864
IS - 6
VL - 16
SN - 1939-1374
SN - 2372-0204
ER -
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BibTex (до 50 авторов)
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@article{2023_Zhang,
author = {Sheng-Lin Zhang and Pengxiang Jin and Zihan Lin and Yong-qian Sun and Bicheng Zhang and Sibo Xia and Zhengdan Li and Zhenyu Zhong and Minghua Ma and Wa Jin and Dai Zhang and Zhenyu Zhu and Dan Pei},
title = {Robust Failure Diagnosis of Microservice System Through Multimodal Data},
journal = {IEEE Transactions on Services Computing},
year = {2023},
volume = {16},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
month = {nov},
url = {https://ieeexplore.ieee.org/document/10165686/},
number = {6},
pages = {3851--3864},
doi = {10.1109/TSC.2023.3290018}
}
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MLA
Скопировать
Zhang, Sheng-Lin, et al. “Robust Failure Diagnosis of Microservice System Through Multimodal Data.” IEEE Transactions on Services Computing, vol. 16, no. 6, Nov. 2023, pp. 3851-3864. https://ieeexplore.ieee.org/document/10165686/.