Outlier Analysis in Live Systems from Application Logs
| Vortragende(r) | Wenrui Zhou | |
|---|---|---|
| Vortragstyp | Masterarbeit | |
| Betreuer(in) | Edouard Fouché | |
| Termin | Fr 17. September 2021 | |
| Vortragssprache | ||
| Vortragsmodus | ||
| Kurzfassung | Modern computer applications tend to generate massive amounts of logs and have become so complex that it is often difficult to explain why applications failed. Locating outliers in application logs can help explain application failures. Outlier detection in application logs is challenging because (1) the log is unstructured text streaming data. (2) labeling application logs is labor-intensive and inefficient.
Logs are similar to natural languages. Recent deep learning algorithm Transformer Neural Network has shown outstanding performance in Natural Language Processing (NLP) tasks. Based on these, we adapt Transformer Neural Network to detect outliers from applications logs In an unsupervised way. We compared our algorithm against state-of-the-art log outlier detection algorithms on three widely used benchmark datasets. Our algorithm outperformed state-of-the-art log outlier detection algorithms. | |