Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?

• URL: http://arxiv.org/abs/2408.13729v2
• Date: Sun, 8 Sep 2024 05:05:45 GMT
• Title: Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?
• Authors: Luan Pham, Huong Ha, Hongyu Zhang,
• Abstract summary: We conduct a comprehensive evaluation of causal inference-based root cause analysis methods for microservice systems. No method stands out in all situations; each method tends to either fall short in effectiveness, efficiency, or shows sensitivity to specific parameters.
• Score: 11.627235799040388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Microservice architecture has become a popular architecture adopted by many cloud applications. However, identifying the root cause of a failure in microservice systems is still a challenging and time-consuming task. In recent years, researchers have introduced various causal inference-based root cause analysis methods to assist engineers in identifying the root causes. To gain a better understanding of the current status of causal inference-based root cause analysis techniques for microservice systems, we conduct a comprehensive evaluation of nine causal discovery methods and twenty-one root cause analysis methods. Our evaluation aims to understand both the effectiveness and efficiency of causal inference-based root cause analysis methods, as well as other factors that affect their performance. Our experimental results and analyses indicate that no method stands out in all situations; each method tends to either fall short in effectiveness, efficiency, or shows sensitivity to specific parameters. Notably, the performance of root cause analysis methods on synthetic datasets may not accurately reflect their performance in real systems. Indeed, there is still a large room for further improvement. Furthermore, we also suggest possible future work based on our findings.

Related papers

• RADICE: Causal Graph Based Root Cause Analysis for System Performance Diagnostic [3.708415881042821]
  Root cause analysis is one of the most crucial operations in software reliability regarding system performance diagnostic. We present a novel causal domain knowledge model representing causal relations about the underlying system components. We then introduce RADICE, an algorithm that through the causal graph discovery, enhancement, refinement, and subtraction processes is able to output a root cause causal sub-graph.
  arXiv  Detail & Related papers  (2025-01-20T15:36:39Z)
• PORCA: Root Cause Analysis with Partially Observed Data [15.007249208547885]
  Root Cause Analysis (RCA) aims at identifying the underlying causes of system faults by uncovering and analyzing the causal structure from complex systems. Previous studies implicitly assume a full observation of the system, which neglect the effect of partial observation. We propose PORCA, a novel RCA framework which can explore reliable root causes under both unobserved confounders and unobserved heterogeneity.
  arXiv  Detail & Related papers  (2024-07-08T12:31:12Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation [137.3520153445413]
  A notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. We evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets. The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes.
  arXiv  Detail & Related papers  (2023-07-11T02:58:10Z)
• DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena [59.291745595756346]
  We propose a set of visual analytics methods that allow humans to participate in the discovery of causal relations associated with windows of time delay. Specifically, we leverage a well-established method, logic-based causality, to enable analysts to test the significance of potential causes. Since an effect can be a cause of other effects, we allow users to aggregate different temporal cause-effect relations found with our method into a visual flow diagram.
  arXiv  Detail & Related papers  (2023-03-12T03:40:21Z)
• CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data [61.088705993848606]
  We introduce CausalBench, a benchmark suite for evaluating causal inference methods on real-world interventional data. CaulBench incorporates biologically-motivated performance metrics, including new distribution-based interventional metrics.
  arXiv  Detail & Related papers  (2022-10-31T13:04:07Z)
• Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition [11.067832313491449]
  In this paper, we formulate the root cause analysis problem as a new causal inference task named intervention recognition. We propose a novel unsupervised causal inference-based method named Causal Inference-based Root Cause Analysis (CIRCA) The performance on a real-world dataset shows that CIRCA can improve the recall of the top-1 recommendation by 25% over the best baseline method.
  arXiv  Detail & Related papers  (2022-06-13T01:45:13Z)
• Feature Recommendation for Structural Equation Model Discovery in Process Mining [0.0]
  We propose a method for finding the set of (aggregated) features with a possible effect on the problem. We have implemented the proposed method as a plugin in ProM and we have evaluated it using two real and synthetic event logs.
  arXiv  Detail & Related papers  (2021-08-13T12:23:01Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• A Survey on Causal Inference [64.45536158710014]
  Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics. Various causal effect estimation methods for observational data have sprung up.
  arXiv  Detail & Related papers  (2020-02-05T21:35:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.