Alharthi, Khalid, Jhumka, Arshad, Di, Sheng, Cappello, Franck and Chuah, Edward (2021) Sentiment analysis based error detection for large-scale systems. In: 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) (DSN'21), Taipei, Taiwan, 21-24 Jun 2021. Published in: 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) ISBN 9781665411943. doi:10.1109/DSN48987.2021.00037 ISSN 1530-0889.

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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.

Item Type:	Conference Item (Paper)
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH):	Sentiment analysis, High performance computing, Machine learning , Error-correcting codes (Information theory)
Journal or Publication Title:	2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
Publisher:	IEEE
ISBN:	9781665411943
ISSN:	1530-0889
Official Date:	6 August 2021
Dates:	Date Event 6 August 2021 Published 2 April 2021 Accepted
DOI:	10.1109/DSN48987.2021.00037
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	16 April 2021
Date of first compliant Open Access:	21 April 2021
Conference Paper Type:	Paper
Title of Event:	51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) (DSN'21)
Type of Event:	Conference
Location of Event:	Taipei, Taiwan
Date(s) of Event:	21-24 Jun 2021
Related URLs:	Organisation

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