Wee, Chee Keong and Zhou, Xujuan and Gururajan, Raj 
ORCID: https://orcid.org/0000-0002-5919-0174 and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Wee, Nathan
(2022)
Adaptive Fault Resolution for Database Replication Systems.
In: 17th International Conference on Advanced Data Mining and Applications (ADMA 2021), 2 Feb - 4 Feb 2022, Sydney, Australia.
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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| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Paper) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Files associated with this item cannot be displayed due to copyright restrictions. |
| Faculty/School / Institute/Centre: | Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -) |
| Faculty/School / Institute/Centre: | Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -) |
| Date Deposited: | 28 Apr 2022 22:57 |
| Last Modified: | 02 Nov 2022 05:23 |
| Uncontrolled Keywords: | Data replication; Database management; Fault resolution; Reinforcement learning |
| Fields of Research (2008): | 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080101 Adaptive Agents and Intelligent Robotics |
| Fields of Research (2020): | 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460201 Artificial life and complex adaptive systems |
| Identification Number or DOI: | https://doi.org/10.1007/978-3-030-95405-5_26 |
| URI: | http://eprints.usq.edu.au/id/eprint/46972 |
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