Summary of Chase: a Causal Heterogeneous Graph Based Framework For Root Cause Analysis in Multimodal Microservice Systems, by Ziming Zhao et al.
CHASE: A Causal Heterogeneous Graph based Framework for Root Cause Analysis in Multimodal Microservice Systems
by Ziming Zhao, Tiehua Zhang, Zhishu Shen, Hai Dong, Xingjun Ma, Xianhui Liu, Yun Yang
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes a novel framework, CHASE, for root cause analysis in microservice systems with multimodal data. The framework uses causal heterogeneous graph-based modeling to identify anomalies during service invocations. This is particularly challenging in enterprise-level microservice systems due to complex dependencies and invocation paths. CHASE encodes related information into representative embeddings and models it using a multimodal invocation graph, followed by anomaly detection on each instance node with attentive message passing from adjacent metric and log nodes. The framework learns from the constructed hypergraph and performs root cause localization. Evaluation on two public microservice datasets shows that CHASE achieves significant performance gains compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding problems in big computer systems that are made up of many small parts (microservices). These systems can be very tricky to understand because there are so many connections between the different parts. The researchers developed a new way to analyze these systems and find the source of any problems. Their method uses special types of computer models and is really good at finding the cause of issues in these complex systems. They tested their method on two real-world datasets and it performed much better than other methods. |
Keywords
* Artificial intelligence * Anomaly detection
