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=Outlier Detection in today’s application logs is a difficult task because such applications generate massive amounts of unstructured logs, and the formate of such logs differs from one application to another. Since logs are similar to natural languages and state-of-the-art deep learning algorithms have achieved fantastic performance in natural language processing, we utilize state-of-the-art seq2seq frameworks and their attention mechanisms to detect and explain outliers in application logs. We test our framework with several outlier detection benchmarks and achieve comparable performance to state-of-the-art log outlier detection frameworks.
+|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.
 }}