LogRCA: Log-based Root Cause Analysis for Distributed Services

• URL: http://arxiv.org/abs/2405.13599v1
• Date: Wed, 22 May 2024 12:50:56 GMT
• Title: LogRCA: Log-based Root Cause Analysis for Distributed Services
• Authors: Thorsten Wittkopp, Philipp Wiesner, Odej Kao,
• Abstract summary: We propose LogRCA, a novel method for identifying a minimal set of log lines that together describe a root cause. LogRCA uses a semi-supervised learning approach to deal with rare and unknown errors and is designed to handle noisy data. We evaluated our approach on a large-scale production log data set of 44.3 million log lines, which contains 80 failures, whose root causes were labeled by experts.
• Score: 4.049637286678329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To assist IT service developers and operators in managing their increasingly complex service landscapes, there is a growing effort to leverage artificial intelligence in operations. To speed up troubleshooting, log anomaly detection has received much attention in particular, dealing with the identification of log events that indicate the reasons for a system failure. However, faults often propagate extensively within systems, which can result in a large number of anomalies being detected by existing approaches. In this case, it can remain very challenging for users to quickly identify the actual root cause of a failure. We propose LogRCA, a novel method for identifying a minimal set of log lines that together describe a root cause. LogRCA uses a semi-supervised learning approach to deal with rare and unknown errors and is designed to handle noisy data. We evaluated our approach on a large-scale production log data set of 44.3 million log lines, which contains 80 failures, whose root causes were labeled by experts. LogRCA consistently outperforms baselines based on deep learning and statistical analysis in terms of precision and recall to detect candidate root causes. In addition, we investigated the impact of our deployed data balancing approach, demonstrating that it considerably improves performance on rare failures.

Related papers

• 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)
• RAGLog: Log Anomaly Detection using Retrieval Augmented Generation [0.0]
  We explore the use of a Retrieval Augmented Large Language Model that leverages a vector database to detect anomalies from logs. To the best of our knowledge, our experiment which we called RAGLog is a novel one and the experimental results show much promise.
  arXiv  Detail & Related papers  (2023-11-09T10:40:04Z)
• RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information [7.861095039299132]
  The need for log anomaly detection is growing, especially in real-world applications. Traditional deep learning-based anomaly detection models require dataset-specific training, leading to corresponding delays. We introduce RAPID, a model that capitalizes on the inherent features of log data to enable anomaly detection without training delays.
  arXiv  Detail & Related papers  (2023-11-09T06:11:44Z)
• Log-based Anomaly Detection based on EVT Theory with feedback [31.949892354842525]
  We present an accurate, lightweight, and adaptive log-based anomaly detection framework, referred to as SeaLog. Our method introduces a Trie-based Detection Agent (TDA) that employs a lightweight, dynamically-growing trie structure for real-time anomaly detection. To enhance TDA's accuracy in response to evolving log data, we enable it to receive feedback from experts.
  arXiv  Detail & Related papers  (2023-06-08T08:34:58Z)
• EvLog: Identifying Anomalous Logs over Software Evolution [31.46106509190191]
  We propose a novel unsupervised approach named Evolving Log extractor (EvLog) to process logs without parsing. EvLog implements an anomaly discriminator with an attention mechanism to identify the anomalous logs and avoid the issue brought by the unstable sequence. EvLog has shown effectiveness in two real-world system evolution log datasets with an average F1 score of 0.955 and 0.847 in the intra-version setting and inter-version setting, respectively.
  arXiv  Detail & Related papers  (2023-06-02T12:58:00Z)
• PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
  We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
  arXiv  Detail & Related papers  (2023-01-25T16:34:43Z)
• Leveraging Log Instructions in Log-based Anomaly Detection [0.5949779668853554]
  We propose a method for reliable and practical anomaly detection from system logs. It overcomes the common disadvantage of related works by building an anomaly detection model with log instructions from the source code of 1000+ GitHub projects. The proposed method, named ADLILog, combines the log instructions and the data from the system of interest (target system) to learn a deep neural network model.
  arXiv  Detail & Related papers  (2022-07-07T10:22:10Z)
• Mining Root Cause Knowledge from Cloud Service Incident Investigations for AIOps [71.12026848664753]
  Root Cause Analysis (RCA) of any service-disrupting incident is one of the most critical as well as complex tasks in IT processes. In this work, we present ICA and the downstream Incident Search and Retrieval based RCA pipeline, built at Salesforce.
  arXiv  Detail & Related papers  (2022-04-21T02:33:34Z)
• LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision [63.08516384181491]
  We present LogLAB, a novel modeling approach for automated labeling of log messages without requiring manual work by experts. Our method relies on estimated failure time windows provided by monitoring systems to produce precise labeled datasets in retrospect. Our evaluation shows that LogLAB consistently outperforms nine benchmark approaches across three different datasets and maintains an F1-score of more than 0.98 even at large failure time windows.
  arXiv  Detail & Related papers  (2021-11-02T15:16:08Z)
• A2Log: Attentive Augmented Log Anomaly Detection [53.06341151551106]
  Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. Existing unsupervised methods need anomaly examples to obtain a suitable decision boundary. We develop A2Log, which is an unsupervised anomaly detection method consisting of two steps: Anomaly scoring and anomaly decision.
  arXiv  Detail & Related papers  (2021-09-20T13:40:21Z)
• Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs [59.04636530383049]
  We propose Logsy, a classification-based method to learn log representations. We show an average improvement of 0.25 in the F1 score, compared to the previous methods.
  arXiv  Detail & Related papers  (2020-08-21T07:26:55Z)

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.