MicroEGRCL: An Edge-Attention-Based Graph Neural Network Approach for Root Cause Localization in Microservice Systems

Ruibo Chen; Jian Ren; Lingfeng Wang; Yanjun Pu; Kaiyuan Yang; Wenjun Wu

doi:10.1007/978-3-031-20984-0_18

MicroEGRCL: An Edge-Attention-Based Graph Neural Network Approach for Root Cause Localization in Microservice Systems

Ruibo Chen
, Jian Ren^*
, Lingfeng Wang
, Yanjun Pu
, Kaiyuan Yang
, Wenjun Wu

^*Corresponding author for this work

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

Microservices architecture has become the latest trend in building modern applications due to its flexibility, scalability, and agility. However, due to the complex interdependencies between microservices, an anomaly in any one service in a microservice system has the potential to propagate along service dependencies and affect multiple services. Therefore, accurate and efficient root cause localization is a significant challenge for current microservice system operation and maintenance. Focusing on this challenge and leveraging the dynamically constructed service call graph, we propose MicroEGRCL, a root cause localization approach based on graph neural networks with an attention mechanism that includes edge feature enhancement. We conducted an experimental evaluation by injecting various types of service anomalies into two microservice benchmarks running in a Kubernetes cluster. The experimental results demonstrate that MicroEGRCL can achieve an average top1 localization accuracy of 87%, exceeding the state-of-the-art baseline approaches.

Original language	English
Title of host publication	Service-Oriented Computing - 20th International Conference, ICSOC 2022, Proceedings
Editors	Javier Troya, Brahim Medjahed, Mario Piattini, Lina Yao, Pablo Fernández, Antonio Ruiz-Cortés
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	264-272
Number of pages	9
ISBN (Print)	9783031209833
DOIs	https://doi.org/10.1007/978-3-031-20984-0_18
State	Published - 2022
Event	20th International Conference on Service-Oriented Computing, ICSOC 2022 - Seville, Spain Duration: 29 Nov 2022 → 2 Dec 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13740 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Service-Oriented Computing, ICSOC 2022
Country/Territory	Spain
City	Seville
Period	29/11/22 → 2/12/22

Keywords

Anomaly detection
Graph neural network
Microservice
Root cause localization

Access to Document

10.1007/978-3-031-20984-0_18

Cite this

Chen, R., Ren, J., Wang, L., Pu, Y., Yang, K., & Wu, W. (2022). MicroEGRCL: An Edge-Attention-Based Graph Neural Network Approach for Root Cause Localization in Microservice Systems. In J. Troya, B. Medjahed, M. Piattini, L. Yao, P. Fernández, & A. Ruiz-Cortés (Eds.), Service-Oriented Computing - 20th International Conference, ICSOC 2022, Proceedings (pp. 264-272). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13740 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-20984-0_18

Chen, Ruibo ; Ren, Jian ; Wang, Lingfeng et al. / MicroEGRCL : An Edge-Attention-Based Graph Neural Network Approach for Root Cause Localization in Microservice Systems. Service-Oriented Computing - 20th International Conference, ICSOC 2022, Proceedings. editor / Javier Troya ; Brahim Medjahed ; Mario Piattini ; Lina Yao ; Pablo Fernández ; Antonio Ruiz-Cortés. Springer Science and Business Media Deutschland GmbH, 2022. pp. 264-272 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Microservices architecture has become the latest trend in building modern applications due to its flexibility, scalability, and agility. However, due to the complex interdependencies between microservices, an anomaly in any one service in a microservice system has the potential to propagate along service dependencies and affect multiple services. Therefore, accurate and efficient root cause localization is a significant challenge for current microservice system operation and maintenance. Focusing on this challenge and leveraging the dynamically constructed service call graph, we propose MicroEGRCL, a root cause localization approach based on graph neural networks with an attention mechanism that includes edge feature enhancement. We conducted an experimental evaluation by injecting various types of service anomalies into two microservice benchmarks running in a Kubernetes cluster. The experimental results demonstrate that MicroEGRCL can achieve an average top1 localization accuracy of 87\%, exceeding the state-of-the-art baseline approaches.",

keywords = "Anomaly detection, Graph neural network, Microservice, Root cause localization",

author = "Ruibo Chen and Jian Ren and Lingfeng Wang and Yanjun Pu and Kaiyuan Yang and Wenjun Wu",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 20th International Conference on Service-Oriented Computing, ICSOC 2022 ; Conference date: 29-11-2022 Through 02-12-2022",

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Chen, R, Ren, J, Wang, L, Pu, Y, Yang, K & Wu, W 2022, MicroEGRCL: An Edge-Attention-Based Graph Neural Network Approach for Root Cause Localization in Microservice Systems. in J Troya, B Medjahed, M Piattini, L Yao, P Fernández & A Ruiz-Cortés (eds), Service-Oriented Computing - 20th International Conference, ICSOC 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13740 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 264-272, 20th International Conference on Service-Oriented Computing, ICSOC 2022, Seville, Spain, 29/11/22. https://doi.org/10.1007/978-3-031-20984-0_18

MicroEGRCL: An Edge-Attention-Based Graph Neural Network Approach for Root Cause Localization in Microservice Systems. / Chen, Ruibo; Ren, Jian; Wang, Lingfeng et al.
Service-Oriented Computing - 20th International Conference, ICSOC 2022, Proceedings. ed. / Javier Troya; Brahim Medjahed; Mario Piattini; Lina Yao; Pablo Fernández; Antonio Ruiz-Cortés. Springer Science and Business Media Deutschland GmbH, 2022. p. 264-272 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13740 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Chen, Ruibo

AU - Ren, Jian

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AU - Pu, Yanjun

AU - Yang, Kaiyuan

AU - Wu, Wenjun

PY - 2022

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AB - Microservices architecture has become the latest trend in building modern applications due to its flexibility, scalability, and agility. However, due to the complex interdependencies between microservices, an anomaly in any one service in a microservice system has the potential to propagate along service dependencies and affect multiple services. Therefore, accurate and efficient root cause localization is a significant challenge for current microservice system operation and maintenance. Focusing on this challenge and leveraging the dynamically constructed service call graph, we propose MicroEGRCL, a root cause localization approach based on graph neural networks with an attention mechanism that includes edge feature enhancement. We conducted an experimental evaluation by injecting various types of service anomalies into two microservice benchmarks running in a Kubernetes cluster. The experimental results demonstrate that MicroEGRCL can achieve an average top1 localization accuracy of 87%, exceeding the state-of-the-art baseline approaches.

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Chen R, Ren J, Wang L, Pu Y, Yang K, Wu W. MicroEGRCL: An Edge-Attention-Based Graph Neural Network Approach for Root Cause Localization in Microservice Systems. In Troya J, Medjahed B, Piattini M, Yao L, Fernández P, Ruiz-Cortés A, editors, Service-Oriented Computing - 20th International Conference, ICSOC 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 264-272. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-20984-0_18