Sentiment Analysis based Error Detection for Large-Scale Systems

Alharthi, Khalid Ayedh; Jhumka, Arshad; Di, Sheng; Cappello, Franck; Chuah, Edward

doi:10.1109/dsn48987.2021.00037

Cited by 11 publications

(12 citation statements)

References 44 publications

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“…Some log anomaly detection workflows employ preprocessing components. These components include NLP functions and filters to remove characters and character sequences (such as punctuation or stop-words) [174]. Another tactic is termsplitting, which aims to separate known tokens written together (for example, splitting the source string "TimeoutException" into separate "Timeout" and "Exception" tokens) .0837 [230].…”

Section: ) Preprocessingmentioning

confidence: 99%

Landscape and Taxonomy of Online Parser-Supported Log Anomaly Detection Methods

Lupton,

Washizaki,

Yoshioka

et al. 2024

IEEE Access

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As production system estates become larger and more complex, ensuring stability through traditional monitoring approaches becomes more challenging. Rule-based monitoring is common in industrial settings, but it has limitations. These include the difficulty of crafting rules capable of detecting unforeseen issues and the burden of manually maintaining rule sets. A potential solution to effectively manage complex system states is log anomaly detection. Workflows for log anomaly detection utilize several fundamental components. These include preprocessors for data cleansing, parsers to extract structured information from raw log data, encoding algorithms to convert extracted data into usable model input features, anomaly detection methods to isolate anomalous signals, and feedback mechanisms to incrementally improve model performance. This study explores the current state of research into online parser-supported log anomaly detection methods, investigates recent research trends, compares the performances of parser and anomaly detection methods using common public datasets and metrics, and assesses their performance evolution over time. Additionally, it classifies available methods using a newly introduced taxonomy, highlights current research gaps, and recommends future research directions.INDEX TERMS Log parsing, log template extraction, online algorithms, anomaly detection.

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Section: ) Preprocessingmentioning

confidence: 99%

Landscape and Taxonomy of Online Parser-Supported Log Anomaly Detection Methods

Lupton,

Washizaki,

Yoshioka

et al. 2024

IEEE Access

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“…In the recent past, ample researchers applied NLP techniques to analyse system Logs, considering that logs are present in the natural language. In the literature, TF-IDF [14], Word2Vec [42] [43], and Glove [32] techniques were used to conduct log analysis, but these techniques failed to handle homophones, homonyms, and out-of-vocabulary words. Due to these limitations, these techniques are unsuitable for unstable and newly generated log records.…”

Section: Log Semantic Embeddingmentioning

confidence: 99%

“…The researchers applied unsupervised anomaly detection on extracted features, which offers a 67.25% improved F1 score over LogCluster [38]. Research has been done [14] to calculate polarity scores and identify the erroneous behaviors in the HPC system with a 96% F-score. The researchers [15] developed a system using the BERT pre-trained model as a transformer encoder to design log sequences.…”

Section: Case Studymentioning

confidence: 99%

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Failure Detection Using Semantic Analysis and Attention-Based Classifier Model for IT Infrastructure Log Data

Bhanage,

Pawar,

Kotecha

et al. 2023

IEEE Access

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The improvement in the reliability, availability, and maintenance of the IT infrastructure components is paramount to ensure uninterrupted services in large-scale IT Infrastructures. The massive system logs generated by infrastructures have proved to be advantageous to pursue the run-time circumstances and behavior of the system. An existing literature has log-based failure detection techniques carrying semantic analysis but on limited log features, reflecting ineffectiveness in anomaly detection for unstable and unseen log records. We have proposed in this paper a semantic log analysis model with three log features to apprehend the gist of the log message. BERT pre-trained model is employed to adapt the feature embedding. The generated numerical vectors are further furnished to train an attention-based OLSTM (Optimized Long Short-Term Memory Networks) classifier to detect failures in diverse infrastructures. The proposed model is evaluated on five different infrastructures: Apache from a server application, OpenStack from the Distributed Systems, Windows from the Operating System, BGL from a Supercomputer, and Android from the Mobile System. The findings illustrate that the proposed system delivers improved and stable results, considering the varied IT infrastructures.

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“…For example, Di et al [39] used a correlation algorithm to remove highly correlated log-events prior to training their modified K-Means clustering technique. Alharti et al [40] also removed the highly correlated log-events prior to training their sentiment analysis model. Therefore, LogCTs are a prerequisite for model training to (a) shorten the training time and (b) increase the prediction power of the prediction model by selecting only the relevant features for training the model [39], [40].…”

Section: ) Model Trainingmentioning

confidence: 99%

A Survey of Log-Correlation Tools for Failure Diagnosis and Prediction in Cluster Systems

et al. 2022

Self Cite

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System logs are the first source of information available to system designers to analyze and troubleshoot their cluster systems. For example, High-Performance Computing (HPC) systems generate a large volume of heterogeneous data from multiple sub-systems, so the idea of using a single source of data to achieve a given goal, such as identification of failures, is losing its validity. System log-analysis tools assist system designers gain understanding into a large volume of system logs. They enable system designers to perform various analyses (e.g., diagnosing node failures or predicting node failures). Current system loganalysis tools vary significantly in their function and design. We conduct a systematic review of literature on system log-analysis tools and select 46 representative articles out of 3,758 initial articles. To the best of our knowledge, there is no work that studied the characteristics of log-correlation tools (LogCTs) with respect to four quality attributes including (a) spurious correlations, (b) correlation threshold settings, (c) outliers in the data and (d) missing data. In this paper, we (a) propose a quality model to evaluate LogCTs and (b) use this quality model to evaluate and recommend current LogCTs. Through our review, we (a) identify papers on LogCTs, (b) build a quality model consisting of the four quality attributes and (c) discuss several open challenges for future research. Our study highlights the advantages and limitations of existing LogCTs and identifies research opportunities that could facilitate better failure handling in large cluster systems.

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Sentiment Analysis based Error Detection for Large-Scale Systems

Cited by 11 publications

References 44 publications

Landscape and Taxonomy of Online Parser-Supported Log Anomaly Detection Methods

Landscape and Taxonomy of Online Parser-Supported Log Anomaly Detection Methods

Failure Detection Using Semantic Analysis and Attention-Based Classifier Model for IT Infrastructure Log Data

A Survey of Log-Correlation Tools for Failure Diagnosis and Prediction in Cluster Systems

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