Logflow: Simplified Log Analysis For Large Scale Systems
PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING AND NETWORKING (ICDCN '21)(2021)
Abstract
Distributed infrastructures generate huge amount of logs that can provide useful information about the state of system, but that can be challenging to analyze. The paper presents LogFlow, a tool to help human operators in the analysis of logs by automatically constructing graphs of correlations between log entries. The core of LogFlow is an interpretable predictive model based on a Recurrent Neural Network augmented with a state-of-the-art attention layer from which correlations between log entries are deduced. To be able to deal with huge amount of data, LogFlow also relies on a new log parser algorithm that can be orders of magnitude faster than best existing log parsers. Experiments rim with several system logs generated by Supercomputers and Cloud systems show that LogFlow is able to achieve more than 96% of accuracy in most cases.
MoreTranslated text
Key words
Log Analysis,System Logs,Intrusion Detection
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined