Adaptive Data Replication Optimization Based on Reinforcement Learning
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Description
Data replication plays an important role in enterprise IT landscapes, where data is shared among multiple IT systems. IT administrators need to tune the replicating software’s configuration setting for it to perform at its optimum level. It is a challenge to continue optimizing the software’s configuration to keep up with the fluctuating workload in a dynamic business environment. We propose a novel approach of using reinforcement learning with meta-heuristics to create an adaptive optimization method for data replication software. The experimental results show the replicating software managed by the proposed approach can perform at an optimum level despite consistently working under changing workloads.
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| ID Code: | 206925 | ||||
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| Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||
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| Measurements or Duration: | 8 pages | ||||
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| Keywords: | reinforcement learning, shareplex, data replication, optimization, metaheuristics, Oracle database | ||||
| DOI: | 10.1109/SSCI47803.2020.9308306 | ||||
| ISBN: | 978-1-7281-2548-0 | ||||
| Pure ID: | 69167789 | ||||
| Divisions: | Current > Research Centres > Centre for Data Science Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Computer Science |
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| Copyright Owner: | Consult author(s) regarding copyright matters | ||||
| Copyright Statement: | 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||
| Deposited On: | 09 Dec 2020 05:01 | ||||
| Last Modified: | 10 Apr 2024 18:59 |
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