Automated Root Cause Analysis System for Complex Data Products
- URL: http://arxiv.org/abs/2412.15374v1
- Date: Thu, 19 Dec 2024 20:10:54 GMT
- Title: Automated Root Cause Analysis System for Complex Data Products
- Authors: Mathieu Demarne, Miso Cilimdzic, Tom Falkowski, Timothy Johnson, Jim Gramling, Wei Kuang, Hoobie Hou, Amjad Aryan, Gayatri Subramaniam, Kenny Lee, Manuel Mejia, Lisa Liu, Divya Vermareddy,
- Abstract summary: We present ARCAS (Automated Root Cause Analysis System), a diagnostic platform built for fast diagnostic implementation and low learning curve.<n>Arcas is composed of a constellation of automated troubleshooting guides (Auto-TSGs) that can execute in parallel to detect issues using product telemetry and apply mitigation in near-real-time.
- Score: 1.7458548956314806
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: We present ARCAS (Automated Root Cause Analysis System), a diagnostic platform based on a Domain Specific Language (DSL) built for fast diagnostic implementation and low learning curve. Arcas is composed of a constellation of automated troubleshooting guides (Auto-TSGs) that can execute in parallel to detect issues using product telemetry and apply mitigation in near-real-time. The DSL is tailored specifically to ensure that subject matter experts can deliver highly curated and relevant Auto-TSGs in a short time without having to understand how they will interact with the rest of the diagnostic platform, thus reducing time-to-mitigate and saving crucial engineering cycles when they matter most. This contrasts with platforms like Datadog and New Relic, which primarily focus on monitoring and require manual intervention for mitigation. ARCAS uses a Large Language Model (LLM) to prioritize Auto-TSGs outputs and take appropriate actions, thus suppressing the costly requirement of understanding the general behavior of the system. We explain the key concepts behind ARCAS and demonstrate how it has been successfully used for multiple products across Azure Synapse Analytics and Microsoft Fabric Synapse Data Warehouse.
Related papers
- TAMO:Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data [33.5606443790794]
Large language models (LLMs) have made breakthroughs in contextual inference and domain knowledge integration.
We propose a tool-assisted LLM agent with multi-modality observation data, namely TAMO, for fine-grained root cause analysis.
arXiv Detail & Related papers (2025-04-29T06:50:48Z) - Exploring LLM-based Agents for Root Cause Analysis [17.053079105858497]
Root cause analysis (RCA) is a critical part of the incident management process.
Large Language Models (LLMs) have been used to perform RCA, but are not able to collect additional diagnostic information.
We present an evaluation of a ReAct agent equipped with retrieval tools, on an out-of-distribution dataset of production incidents collected at Microsoft.
arXiv Detail & Related papers (2024-03-07T00:44:01Z) - 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) - Root Cause Analysis In Microservice Using Neural Granger Causal
Discovery [12.35924469567586]
We propose RUN, a novel approach for root cause analysis using neural Granger causal discovery with contrastive learning.
RUN enhances the backbone encoder by integrating contextual information from time series, and leverages a time series forecasting model to conduct neural Granger causal discovery.
In addition, RUN incorporates Pagerank with a vector to efficiently recommend the top-k root causes.
arXiv Detail & Related papers (2024-02-02T04:43:06Z) - AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning [54.47116888545878]
AutoAct is an automatic agent learning framework for QA.
It does not rely on large-scale annotated data and synthetic planning trajectories from closed-source models.
arXiv Detail & Related papers (2024-01-10T16:57:24Z) - RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models [46.476439550746136]
Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently.
We present RCAgent, a tool-augmented LLM autonomous agent framework for practical and privacy-aware industrial RCA usage.
Running on an internally deployed model rather than GPT families, RCAgent is capable of free-form data collection and comprehensive analysis with tools.
arXiv Detail & Related papers (2023-10-25T03:53:31Z) - PyRCA: A Library for Metric-based Root Cause Analysis [66.72542200701807]
PyRCA is an open-source machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps)
It provides a holistic framework to uncover the complicated metric causal dependencies and automatically locate root causes of incidents.
arXiv Detail & Related papers (2023-06-20T09:55:10Z) - RESAM: Requirements Elicitation and Specification for Deep-Learning
Anomaly Models with Applications to UAV Flight Controllers [24.033936757739617]
We present RESAM, a requirements process that integrates knowledge from domain experts, discussion forums, and formal product documentation.
We present a case-study based on a flight control system for small Uncrewed Aerial Systems and demonstrate that its use guides the construction of effective anomaly detection models.
arXiv Detail & Related papers (2022-07-18T18:09:59Z) - 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) - SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines.
This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
arXiv Detail & Related papers (2021-12-22T14:45:37Z) - 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) - How Can Subgroup Discovery Help AIOps? [0.0]
We study how Subgroup Discovery can help AIOps.
This project involves both data mining researchers and practitioners from Infologic, a French software editor.
arXiv Detail & Related papers (2021-09-10T14:41:02Z) - PyODDS: An End-to-end Outlier Detection System with Automated Machine
Learning [55.32009000204512]
We present PyODDS, an automated end-to-end Python system for Outlier Detection with Database Support.
Specifically, we define the search space in the outlier detection pipeline, and produce a search strategy within the given search space.
It also provides unified interfaces and visualizations for users with or without data science or machine learning background.
arXiv Detail & Related papers (2020-03-12T03:30:30Z)
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.