Name
Abstract | ||
|---|---|---|
Logs are one of the most critical data for service management. It contains rich runtime information for both services and users. Since size of logs are often enormous in size and have free handwritten constructions, a typical log-based analysis needs to parse logs into structured format first. However, we observe that most existing log parsing methods cannot parse logs online, which is essential for online services. In this paper, we present an automatic online log parsing method, name as LogStamp. We extensively evaluate LogStamp on five public datasets to demonstrate the effectiveness of our proposed method. The experiments show that our proposed method can achieve high accuracy with only a small portion of the training set. For example, it can achieve an average accuracy of 0.956 when using only 10% of the data training. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3543146.3543168 | SIGMETRICS Performance Evaluation Review |
DocType | Volume | Issue |
Journal | 49 | 4 |
ISSN | Citations | PageRank |
0163-5999 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shimin Tao | 1 | 0 | 4.73 |
| Weibin Meng | 2 | 0 | 0.34 |
| Yimeng Chen | 3 | 0 | 4.06 |
| Yichen Zhu | 4 | 1 | 0.69 |
| Ying Liu Chunning Du | 5 | 0 | 0.34 |
| Tao Han | 6 | 1 | 2.63 |
| Yongpeng Zhao | 7 | 0 | 1.01 |
| Xiangguang Wang | 8 | 0 | 0.34 |
| Hao Yang | 9 | 9 | 13.48 |