Home > Research > Publications & Outputs > Sentiment Analysis based Error Detection for La...

Electronic data

View graph of relations

Sentiment Analysis based Error Detection for Large-Scale Systems

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Published
  • Khalid Alharthi
  • Arshad Jhumka
  • Sheng Di
  • Franck Cappello
  • Edward Chuah
Close
Publication date24/06/2021
<mark>Original language</mark>English
EventThe 51st IEEE/IFIP International Conference on Dependable Systems and Networks - Taipei, Taiwan, Province of China
Duration: 21/06/202124/06/2021
https://dsn2021.ntu.edu.tw/

Conference

ConferenceThe 51st IEEE/IFIP International Conference on Dependable Systems and Networks
Abbreviated titleDSN 2021
CountryTaiwan, Province of China
CityTaipei
Period21/06/2124/06/21
Internet address

Abstract

Today’s large-scale systems such as High Performance Computing (HPC) Systems are designed/utilized towards exascale computing, inevitably decreasing its reliability due to the increasing design complexity. HPC systems conduct extensive logging of their execution behaviour. In this paper, we leverage the inherent meaning behind the log messages and propose a novel sentiment analysis-based approach for the error detection in large-scale systems, by automatically mining the sentiments in the log messages. Our contributions are four-fold. (1) We develop a machine learning (ML) based approach to automatically build a sentiment lexicon, based on the system log message templates. (2) Using the sentiment lexicon, we develop an algorithm to detect system errors. (3) We develop an algorithm to identify the nodes and components with erroneous behaviors, based on sentiment polarity scores. (4) We evaluate our solution vs. other state-of-the-art machine/deep learning algorithms based on three representative supercomputers’ system logs. Experiments show that our error detection algorithm can identify error messages with an average MCC score and f-score of 91% and 96% respectively, while state of the art ML/deep learning
model (LSTM) obtains only 67% and 84%. To the best of our knowledge, this is the first work leveraging the sentiments embedded in log entries of large-scale systems for system health analysis.