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Graph based Incident Extraction and Diagnosis in Large-Scale Online Systems

Authors:

Fangyuan LiAuthors Info & Claims

ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

Article No.: 48, Pages 1 - 13

https://doi.org/10.1145/3551349.3556904

Published: 05 January 2023 Publication History

Abstract

With the ever increasing scale and complexity of online systems, incidents are gradually becoming commonplace. Without appropriate handling, they can seriously harm the system availability. However, in large-scale online systems, these incidents are usually drowning in a slew of issues (i.e., something abnormal, while not necessarily an incident), rendering them difficult to handle. Typically, these issues will result in a cascading effect across the system, and a proper management of the incidents depends heavily on a thorough analysis of this effect. Therefore, in this paper, we propose a method to automatically analyze the cascading effect of availability issues in online systems and extract the corresponding graph based issue representations incorporating both of the issue symptoms and affected service attributes. With the extracted representations, we train and utilize a graph neural networks based model to perform incident detection. Then, for the detected incident, we leverage the PageRank algorithm with a flexible transition matrix design to locate its root cause. We evaluate our approach using real-world data collected from the WeChat online service system, the largest instant message system in China. The results confirm the effectiveness of our approach. Moreover, our approach is successfully deployed in the company and eases the burden of operators in the face of a flood of issues and related alert signals.

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Cited By

Pham LHa HZhang HFilkov VRay BZhou M(2024)Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695065(706-715)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695065
Pham LHa HZhang H(2024)BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point DetectionProceedings of the ACM on Software Engineering10.1145/36608051:FSE(2214-2237)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660805
Yu GChen PHe ZYan QLuo YLi FZheng Z(2024)ChangeRCA: Finding Root Causes from Software Changes in Large Online SystemsProceedings of the ACM on Software Engineering10.1145/36437281:FSE(24-46)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643728
Show More Cited By

Index Terms

Graph based Incident Extraction and Diagnosis in Large-Scale Online Systems
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

October 2022

2006 pages

ISBN:9781450394758

DOI:10.1145/3551349

Copyright © 2022 ACM.

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Published: 05 January 2023

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the National Natural Science Foundation of China
the Natural Science Foundation of Guangdong Province
the Wechat Rhino-Bird Joint Research Program
the Key-Area Research and Development Program of Guangdong Province
the Basic and Applied Basic Research of Guangzhou

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ASE '22

ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering

October 10 - 14, 2022

MI, Rochester, USA

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Overall Acceptance Rate 82 of 337 submissions, 24%

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Cited By

Pham LHa HZhang HFilkov VRay BZhou M(2024)Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695065(706-715)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695065
Pham LHa HZhang H(2024)BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point DetectionProceedings of the ACM on Software Engineering10.1145/36608051:FSE(2214-2237)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660805
Yu GChen PHe ZYan QLuo YLi FZheng Z(2024)ChangeRCA: Finding Root Causes from Software Changes in Large Online SystemsProceedings of the ACM on Software Engineering10.1145/36437281:FSE(24-46)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643728
Chen HChen PYu GLi XHe ZZhang H(2024)MicroFI: Non-Intrusive and Prioritized Request-Level Fault Injection for Microservice ApplicationsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.3363902(1-18)Online publication date: 2024
https://doi.org/10.1109/TDSC.2024.3363902
Chakraborty SAgarwal SGarg SSethia APandey UAggarwal VSaini S(2023)ESRO: Experience Assisted Service Reliability against Outages2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00131(255-267)Online publication date: 11-Sep-2023
https://doi.org/10.1109/ASE56229.2023.00131

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