ML-Based System Failure Prediction Using Resource Utilization
4
Issued Date
2023-01-01
Resource Type
ISSN
23673370
eISSN
23673389
Scopus ID
2-s2.0-85151065399
Journal Title
Lecture Notes in Networks and Systems
Volume
611 LNNS
Start Page
40
End Page
50
Rights Holder(s)
SCOPUS
Bibliographic Citation
Lecture Notes in Networks and Systems Vol.611 LNNS (2023) , 40-50
Suggested Citation
Rassameeroj I., Khajohn-udomrith N., Ngamjaruskotchakorn M., Kirdsaeng T., Khongchuay P. ML-Based System Failure Prediction Using Resource Utilization. Lecture Notes in Networks and Systems Vol.611 LNNS (2023) , 40-50. 50. doi:10.1007/978-3-031-27470-1_5 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/81791
Title
ML-Based System Failure Prediction Using Resource Utilization
Author's Affiliation
Other Contributor(s)
Abstract
Digital transactions are growing exponentially on a regular basis without any sign of interruption. Unfortunately, most online service providers were not able to operate 24/7 every day due to the limitation of system components. Online service providers can be highly benefited if an accurate system failure prediction can be obtained. The adverse effect of computer failure might be mitigated if a proper prediction could be made beforehand. In this paper, we propose a simple model training approach to detect the failure that might arise in a system by parsing the log files and conducting a probabilistic analysis of future performance values in advance. We utilize a Recurrent Neural Networks (RNN), namely, Long Short-Term Memory (LSTM) to provide the optimal solution for predicting system failure by reckoning the hardware performance utilization value and utilizing the prediction of log data system for representing the system benchmark. Consequently, the significant constituents that are considered for calculation are all utilization of CPU, MEM, DISK, and NET. Apart from utilization, another essential constituent that needs to examine is System Callout, which is a representative for displaying the alert signal to inform whenever the information system should ignore the incoming transactions to maintain several server systems.