Outlier Analysis in Live Systems from Application Logs: Unterschied zwischen den Versionen

Aktuelle Version vom 13. September 2021, 21:13 Uhr

Vortragende(r)	Wenrui Zhou
Vortragstyp	Masterarbeit
Betreuer(in)	Edouard Fouché
Termin	Fr 17. September 2021
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.

@@ Zeile 2: / Zeile 2: @@
 |vortragender=Wenrui Zhou
 |email=unvri@student.kit.edu
-|vortragstyp=Proposal
+|vortragstyp=Masterarbeit
 |betreuer=Edouard Fouché
-|termin=Institutsseminar/2021-06-11
+|termin=Institutsseminar/2021-09-17
-|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.
+|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.
-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.
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