Adaptive Fault Diagnosis for Data Replication Systems
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Description
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.
Impact and interest:
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| ID Code: | 207312 | ||
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| Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||
| Series Name: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | ||
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| Measurements or Duration: | 14 pages | ||
| DOI: | 10.1007/978-3-030-69377-0_11 | ||
| ISBN: | 978-3-030-69376-3 | ||
| Pure ID: | 73581099 | ||
| Copyright Owner: | 2021 Springer Nature Switzerland AG | ||
| Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||
| Deposited On: | 14 Jan 2021 01:39 | ||
| Last Modified: | 29 Feb 2024 15:04 |
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