Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment

Lin, Fred; Muzumdar, Keyur; Laptev, Nikolay; Curelea, Mihai-Valentin; Lee, Seunghak; Sankar, Sriram

doi:10.1145/3393691.3394185

Cited by 14 publications

(2 citation statements)

References 3 publications

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“…Before attempting INDRA-Eidos, other approaches were tested. One approach is Fast dimensional analysis for root cause investigation 12 . There are several tools for dimensional reduction analysis, one of them is Factor Analysis which is an unsupervised machine learning algorithm for dimensional reduction.…”

Section: Early Attempts For Root Cause and Association Rule Extractio...mentioning

confidence: 99%

Taxonomical Modeling and Classification in Space Hardware Failure Reporting

Palacios

Hill

2023

Preprint

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NASA Johnson Space Center has collected more than 54,000 space hardware failure reports. Obtaining engineering processes trends or root cause analysis by manual inspection is impractical. Fortunately, novel data science tools in Machine Learning and Natural Language Processing (NLP) can be utilized to perform text mining and knowledge extraction. In NLP the use of taxonomies (classification trees) are key to the structuring of text data, extracting knowledge and important concepts from documents, and facilitating the identification of correlations and trends within the data set. Usually, these taxonomies and text structures live in the heads of experts in their specific field. However, when an expert is not available, taxonomies and ontologies are not found in data bases, or the field of study is too broad, this approach can enable and provide structure to the text content of a record set. In this paper an automated taxonomical model is presented by the combination of Latent Dirichlet Allocation (LDA) algorithms and Bidirectional Encoder Representations from Transformers (BERT). Additionally, the limitations and outcomes of causal relationship rule mining models, commercial tools, and deep neural networks are also discussed.

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Section: Early Attempts For Root Cause and Association Rule Extractio...mentioning

confidence: 99%

Taxonomical Modeling and Classification in Space Hardware Failure Reporting

Palacios

Hill

2023

Preprint

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“…Contrast set mining has limited power compared to Minesweeper, because it does not have any representation for temporal events. Another related work, by Lin et al [10], uses frequent itemset mining to find the subset of columns/features in a log, which all occur in multiple rows (which is the support of this item set) and are correlated with failures. Their focus is on scalability and interpretability.…”

Section: Related Workmentioning

confidence: 99%

Scalable Statistical Root Cause Analysis on App Telemetry

Murali¹,

Yao²,

Mathur³

et al. 2020

Preprint

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Despite engineering workflows that aim to prevent buggy code from being deployed, bugs still make their way into the Facebook app. When symptoms of these bugs, such as user submitted reports and automatically captured crashes, are reported, finding their root causes is an important step in resolving them. However, at Facebook's scale of billions of users, a single bug can manifest as several different symptoms according to the various user and execution environments in which the software is deployed. Root cause analysis (RCA) therefore requires tedious manual investigation and domain expertise to extract out common patterns that are observed in groups of reports and use them for debugging.In this paper, we propose Minesweeper, a technique for RCA that moves towards automatically identifying the root cause of bugs from their symptoms. The method is based on two key aspects: (i) a scalable algorithm to efficiently mine patterns from telemetric information that is collected along with the reports, and (ii) statistical notions of precision and recall of patterns that help point towards root causes. We evaluate Minesweeper on its scalability and effectiveness in finding root causes from symptoms on real world bug and crash reports from Facebook's apps. Our evaluation demonstrates that Minesweeper can perform RCA for tens of thousands of reports in less than 3 minutes, and is more than 85% accurate in identifying the root cause of regressions.

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