Adaptive Fault Diagnosis for Data Replication Systems
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 12610 LNCS, Page: 125-138
2021
- 2Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
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Conference Paper 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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85102510006&origin=inward; http://dx.doi.org/10.1007/978-3-030-69377-0_11; http://link.springer.com/10.1007/978-3-030-69377-0_11; http://link.springer.com/content/pdf/10.1007/978-3-030-69377-0_11; https://dx.doi.org/10.1007/978-3-030-69377-0_11; https://link.springer.com/chapter/10.1007/978-3-030-69377-0_11
Springer Science and Business Media LLC
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