Adaptive fault diagnosis for data replication systems

Paper

Wee, Chee Keong and Wee, Nathan. 2021. "Adaptive fault diagnosis for data replication systems." Qiao, Miao, Vossen, Gottfried, Wang, Sen and Li, Lei (ed.) 32nd Australasian Database Conference: Database Theory and Applications (ADC 2021). Dunedin, New Zealand 29 Jan - 05 Feb 2021 Cham, Switzerland. Springer. https://doi.org/10.1007/978-3-030-69377-0_11
Paper/Presentation Title

Adaptive fault diagnosis for data replication systems

Presentation TypePaper
AuthorsWee, Chee Keong (Author) and Wee, Nathan (Author)
EditorsQiao, Miao, Vossen, Gottfried, Wang, Sen and Li, Lei
Journal or Proceedings TitleLecture Notes in Computer Science (Book series)
ERA Conference ID42492
Journal Citation12610, pp. 125-138
Number of Pages14
Year2021
PublisherSpringer
Place of PublicationCham, Switzerland
ISSN1611-3349
0302-9743
ISBN9783030693763
9783030693770
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-69377-0_11
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-3-030-69377-0_11
Conference/Event32nd Australasian Database Conference: Database Theory and Applications (ADC 2021)
Australasian Database Conference
Event Details
Australasian Database Conference
ADC
Rank
B
B
B
B
Event Details
32nd Australasian Database Conference: Database Theory and Applications (ADC 2021)
Event Date
29 Jan 2021 to end of 05 Feb 2021
Event Location
Dunedin, New Zealand
Abstract

Data replication among multiple IT systems is ubiquitous among large organizations and keeping them running is a critical success factor for their IT departments. When services are disrupted, IT administrators must be able to find the faults and rectify them quickly. Due to the scale and complexity of the data replication environment, the fault diagnostic effort is both tedious and laborious. This paper proposes an approach to fault diagnosis of the data replication software through deep reinforcement learning. Empirical results show that the new method can identify and deduce the software faults quickly with high accuracy.

Keywordsdatabase systems; deep learning; failure analysis; reinforcement learning
ANZSRC Field of Research 2020460201. Artificial life and complex adaptive systems
460505. Database systems
460501. Data engineering and data science
Byline AffiliationsEnergy Queensland, Australia
University of Queensland
Institution of OriginUniversity of Southern Queensland
Permalink -

https://research.usq.edu.au/item/q7138/adaptive-fault-diagnosis-for-data-replication-systems

Log in to edit
  • 70
    total views
  • 4
    total downloads
  • 2
    views this month
  • 0
    downloads this month

Export as

Related outputs

Adaptive Fault Resolution for Database Replication Systems
Wee, Chee Keong, Zhou, Xujuan, Gururajan, Raj, Tao, Xiaohui and Wee, Nathan. 2022. "Adaptive Fault Resolution for Database Replication Systems." Li, Bohan, Yue, Lin, Jiang, Jing, Chen, Weitong, Li, Xue, Long, Guodong, Fang, Fei and Yu, Han (ed.) 17th International Conference on Advanced Data Mining and Applications (ADMA 2021). Sydney, Australia 02 - 04 Feb 2022 Berlin. Springer. https://doi.org/10.1007/978-3-030-95405-5_26
Adaptive fault diagnosis and resolution system for enterprise data replication system using deep reinforcement learning
Wee, Chee Keong. 2022. Adaptive fault diagnosis and resolution system for enterprise data replication system using deep reinforcement learning. PhD Thesis Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/q7486