Outlier Analysis in Live Systems from Application Logs
|Termin||Fr 11. Juni 2021|
|Kurzfassung||Modern computer applications tend to generate massive amounts logs and have become so complex that it often is difficult to explain why a specific application has failed. In this work we want detect and explain such failure by detecting outliers from application logs. This is challenging because (1)Log is unstructured text streaming data. (2)labelling application log is labor intensive and inefficient.
Logs are similar to natural languages. Recent advances in deep learning have shown great performance in Natural Language Processing (NLP) tasks. Based on these, we investigate how state-of-the-art sequence-to-sequence frameworks with attention mechanisms can detect and expolain outliers from applications logs. We plan to compare our framework against state-of-the-art log outlier detectors, based on existing outlier detection benchmarks.