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Deep Neural Network Based Log Analysis

被引：0
|
作者：
Ketmen, Hasan Burak [1 ]
Bulut, Baris [1 ]
机构：
[1] Enforma Bilisim AS, Istanbul, Turkey
来源：
29TH IEEE CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS (SIU 2021) | 2021年
关键词：
DevOps; Jenkins; log analysis; natural language sequence; Bidirectional Long Short Term Memory;
D O I：
10.1109/SIU53274.2021.9477836
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Today, the logs of the services running on virtual and real servers provide us with data from which statistical inferences can be made about the service itself and the ecosystem it is in, if it is analyzed. The usage purposes of the tools used in DevOps, the content of the logs they produce, the way they are stored and the frequency of reproduction may vary. Creating metrics by analyzing logs and taking necessary actions on metrics can potentially increase productivity when the hurdles such as fragmentation of unstructured data, processing of data and processing of data are overcome. In this study, it is shown that the logs can be modeled as a natural language sequence in order that the necessary actions be taken, and it is possible to classify the process logs of Jenkins, which is one of the services used in DevOps, by using the Bidirectional Long Short-Term Memory method, with high accuracy and Cohen's Kappa score.
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页数：4
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