Paper Info
Title
Sentiment Analysis based Error Detection for Large-Scale Systems
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.
Year
DOI
Venue
2021
10.1109/DSN48987.2021.00037
2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
Keywords
DocType
ISSN
Sentiment analysis lexicon,large-scale systems,Stochastic Gradient Descent,logistic regression,error detection
Conference
1530-0889
ISBN
Citations 
PageRank 
978-1-6654-1194-3
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Khalid Ayedh Alharthi100.34
Arshad Jhumka215.42
Sheng Di373755.88
Franck Cappello43775251.47
Edward Chuah500.34