Brain: Log parsing with bidirectional parallel tree
Automated log analysis can facilitate failure diagnosis for developers and operators using a
large volume of logs. Log parsing is a prerequisite step for automated log analysis, which …
large volume of logs. Log parsing is a prerequisite step for automated log analysis, which …
Robust failure diagnosis of microservice system through multimodal data
Automatic failure diagnosis is crucial for large microservice systems. Currently, most failure
diagnosis methods rely solely on single-modal data (ie, using either metrics, logs, or traces) …
diagnosis methods rely solely on single-modal data (ie, using either metrics, logs, or traces) …
LogKG: Log Failure Diagnosis through Knowledge Graph
Y Sui, Y Zhang, J Sun, T Xu, S Zhang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Logs are one of the most valuable data to describe the running state of services. Failure
diagnosis through logs is crucial for service reliability and security. The current automatic log …
diagnosis through logs is crucial for service reliability and security. The current automatic log …
Self-supervised log parsing using semantic contribution difference
Logs can help developers to promptly diagnose software system failures. Log parsers, which
parse semi-structured logs into structured log templates, are the first component for …
parse semi-structured logs into structured log templates, are the first component for …
Log Parsing with Generalization Ability under New Log Types
Log parsing, which converts semi-structured logs into structured logs, is the first step for
automated log analysis. Existing parsers are still unsatisfactory in real-world systems due to …
automated log analysis. Existing parsers are still unsatisfactory in real-world systems due to …
Multivariate Log-based Anomaly Detection for Distributed Database
Distributed databases are fundamental infrastructures of today's large-scale software
systems such as cloud systems. Detecting anomalies in distributed databases is essential …
systems such as cloud systems. Detecting anomalies in distributed databases is essential …
Augmenting log-based anomaly detection models to reduce false anomalies with human feedback
T Jia, Y Li, Y Yang, G Huang, Z Wu - Proceedings of the 28th ACM …, 2022 - dl.acm.org
With the increasing complexity of modern software systems, it is essential yet hard to detect
anomalies and diagnose problems precisely. Existing log-based anomaly detection …
anomalies and diagnose problems precisely. Existing log-based anomaly detection …
Holistic Root Cause Analysis for Failures in Cloud-Native Systems Through Observability Data
Y Han, Q Du, Y Huang, P Li, X Shi, J Wu… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Microservices are widely adopted in large IT enterprises, leveraging the scalability,
resiliency, and elasticity of the cloud-native architecture. Effective root cause analysis is …
resiliency, and elasticity of the cloud-native architecture. Effective root cause analysis is …
Logsummary: Unstructured log summarization for software systems
We propose LogSummary, an automatic, unsupervised end-to-end log summarization
framework for software system maintenance in this work. LogSummary obtains the …
framework for software system maintenance in this work. LogSummary obtains the …
Glad: Content-aware dynamic graphs for log anomaly detection
Logs play a crucial role in system monitoring and debugging by recording valuable system
information, including events and states. Although various methods have been proposed to …
information, including events and states. Although various methods have been proposed to …