Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Root Cause Analysis in Microservice Using Neural Granger Causal Discovery
Authors: Cheng-Ming Lin, Ching Chang, Wei-Yao Wang, Kuang-Da Wang, Wen-Chih Peng
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on the synthetic and realworld microservice-based datasets demonstrate that RUN noticeably outperforms the state-of-the-art root cause analysis methods. |
| Researcher Affiliation | Academia | Cheng-Ming Lin, Ching Chang, Wei-Yao Wang, Kuang-Da Wang, Wen-Chih Peng National Yang Ming Chiao Tung University, Hsinchu, Taiwan EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes its method in Section 4 'Methodology' but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/zmlin1998/RUN. |
| Open Datasets | No | Dataset As no publicly available real-world dataset for root cause analysis is accessible due to data confidentiality, we test on a synthetic dataset and a test bed utilizing an actual microservice-based application. Sock-shop (Daniel Holbach 2022): The framework of sock-shop encompasses a total of 13 microservices, each developed using distinct technologies. |
| Dataset Splits | No | The paper mentions characteristics of the datasets but does not provide specific details on training, validation, or test splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | We implement our method on a machine with AMD EPYC 7302 16-Core CPU, NVIDIA RTX A5000 graphics cards. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and setting learning rates/batch sizes but does not provide specific software library names with version numbers (e.g., 'PyTorch 1.9') which are typically considered software dependencies. |
| Experiment Setup | Yes | In the time series forecasting stage, the window size w is set to 32. We use the Adam (Kingma and Ba 2015) optimizer and set the learning rate as 0.001 and the batch size as 128. In the causal graph discovery stage, the threshold H is set to 0.5. The training epochs of the pre-training and fine-tuning stages are set to 50. In the diagnosis stage, we set the value of the personalization vector Pd as 1 and Pn as 0.5, similar to (Wang et al. 2021). The k in HR@k is set to 1, 3, and 5. |