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Adaptive Fault Resolution for Database Replication Systems

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Advanced Data Mining and Applications (ADMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13087))

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Abstract

Database replication is ubiquitous among organizations’ IT infrastructure when data is shared across multiple systems and their service uptime is critical. But complex software will eventually suffer outages due to different types of circumstances and it is important to resolve them promptly and restore the services. This paper proposes an approach to resolve data replication software’s through deep reinforcement learning. Empirical results show that the new method can resolve software faults quickly with high accuracy.

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Author information

Authors and Affiliations

  1. Digital Application Services, eHealth Queensland, Brisbane, QLD, Australia

    Chee Keong Wee

  2. School of Business, University of Southern Queensland, Darling Heights, QLD, Australia

    Xujuan Zhou, Raj Gururajan & Xiaohui Tao

  3. Dialog Information Technology, South Brisbane, QLD, Australia

    Nathan Wee

Authors
  1. Chee Keong Wee
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  2. Xujuan Zhou
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  3. Raj Gururajan
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  4. Xiaohui Tao
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  5. Nathan Wee
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Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  2. University of Queensland, Brisbane, QLD, Australia

    Lin Yue

  3. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  4. University of Queensland, Brisbane, QLD, Australia

    Weitong Chen

  5. University of Queensland, Brisbane, QLD, Australia

    Xue Li

  6. University of Technology Sydney, Sydney, NSW, Australia

    Guodong Long

  7. Carnegie Mellon University, Pittsburgh, USA

    Fei Fang

  8. Nanyang Technological University, Singapore, Singapore

    Han Yu

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Wee, C.K., Zhou, X., Gururajan, R., Tao, X., Wee, N. (2022). Adaptive Fault Resolution for Database Replication Systems. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13087. Springer, Cham. https://doi.org/10.1007/978-3-030-95405-5_26

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  • DOI: https://doi.org/10.1007/978-3-030-95405-5_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95404-8

  • Online ISBN: 978-3-030-95405-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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