Summary of Root Cause Analysis in Microservice Using Neural Granger Causal Discovery, by Cheng-ming Lin et al.
Root Cause Analysis In Microservice Using Neural Granger Causal Discovery
by Cheng-Ming Lin, Ching Chang, Wei-Yao Wang, Kuang-Da Wang, Wen-Chih Peng
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of identifying the root cause of system malfunctions in microservices-based applications. Current methods struggle to capture temporal relationships and ignore rich information inherent in time series data. The proposed approach, RUN, uses neural Granger causal discovery with contrastive learning to enhance the backbone encoder and leverage a time series forecasting model for more accurate root cause analysis. This innovative method outperforms state-of-the-art approaches on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists develop a new way to figure out why problems occur in computer systems that use lots of small services working together (microservices). The current methods are not good at understanding the timing of events or using the extra information from time series data. This new method, called RUN, uses artificial intelligence and machine learning to analyze the relationships between different parts of the system and find the root cause of problems more accurately. |
Keywords
* Artificial intelligence * Encoder * Machine learning * Time series
