Current - Issue
Year 2026 · Volume 6 · Issue 5
Original Article
ACDO-RL: Safe Reinforcement Learning for Joint Cloud Database Resource and Index Optimization
Dr. B. Vishnuvardhan1
1 Department of Computer Science, Kakatiya University School of Learning, Kakatiya University, Warangal, Telangana, India.
Published Online: September-October 2026
Pages: 136-144
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260605016References
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Hot Topics Netw. (HotNets), 2016, pp. 50–56.
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management systems,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2018, pp. 631–645.
19. Sharma, F. M. Schuhknecht, and J. Dittrich, “The case for automatic database administration using deep reinforcement learning,”
arXiv:1801.05643, 2018.
20. Kossmann, A. Kastius, and R. Schlosser, “SWIRL: Selection of workload-aware indexes using reinforcement learning,” in Proc. 25th Int.
Conf. Extending Database Technol. (EDBT), 2022, pp. 155–168.
21. R. Marcus et al., “Neo: A learned query optimizer,” Proc. VLDB Endow., vol. 12, no. 11, pp. 1705–1718, 2019.
22. R. Marcus and O. Papaemmanouil, “Deep reinforcement learning for join order enumeration,” in Proc. 1st Int. Workshop Exploiting Artif.
Intell. Techn. Data Manage. (aiDM), 2018, pp. 1–4.
23. S. Krishnan, Z. Yang, K. Goldberg, J. Hellerstein, and I. Stoica, “Learning to optimize join queries with deep reinforcement learning,”
arXiv:1808.03196, 2018.
24. T. Kraska, A. Beutel, E. H. Chi, J. Dean, and N. Polyzotis, “The case for learned index structures,” in Proc. ACM SIGMOD Int. Conf.
Manage. Data, 2018, pp. 489–504.
25. T. Kraska et al., “SageDB: A learned database system,” in Proc. 9th Biennial Conf. Innovative Data Syst. Res. (CIDR), 2019.
26. Alshiekh, R. Bloem, R. Ehlers, B. Könighofer, S. Niekum, and U. Topcu, “Safe reinforcement learning via shielding,” in Proc. 32nd AAAI
Conf. Artif. Intell., 2018, pp. 2669–2678.
27. García and F. Fernández, “A comprehensive survey on safe reinforcement learning,” J. Mach. Learn. Res., vol. 16, pp. 1437 –1480, 2015.
28. Dulac-Arnold et al., “Challenges of real-world reinforcement learning: Definitions, benchmarks and analysis,” Mach. Learn., vol. 110, no.
9, pp. 2419–2468, 2021.
29. Y. Sakasegawa, “An approximation formula Lq ≃ αρβ/(1 − ρ),” Ann. Inst. Stat. Math., vol. 29, no. 1, pp. 67–75, 1977.
30. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. Int. Conf. Learn. Represent. (ICLR), 2015.
2. Pavlo et al., “Self-driving database management systems,” in Proc. 8th Biennial Conf. Innovative Data Syst. Res. (CIDR), 2017.
3. Van Aken, A. Pavlo, G. J. Gordon, and B. Zhang, “Automatic database management system tuning through large-scale machine learning,”
in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2017, pp. 1009–1024.
4. J. Zhang et al., “An end-to-end automatic cloud database tuning system using deep reinforcement learning,” in Proc. ACM SIGMOD Int.
Conf. Manage. Data, 2019, pp. 415–432.
5. Li, X. Zhou, S. Li, and B. Gao, “QTune: A query-aware database tuning system with deep reinforcement learning,” Proc. VLDB Endow.,
vol. 12, no. 12, pp. 2118–2130, 2019.
6. Gandhi, M. Harchol-Balter, R. Raghunathan, and M. A. Kozuch, “AutoScale: Dynamic, robust capacity management for multi-tier data
centers,” ACM Trans. Comput. Syst., vol. 30, no. 4, pp. 1–26, 2012.
7. K. Rzadca et al., “Autopilot: Workload autoscaling at Google,” in Proc. 15th Eur. Conf. Comput. Syst. (EuroSys), 2020, pp. 1–16.
8. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Press, 2018.
9. V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015.
10. J. C. H. Watkins and P. Dayan, “Q-learning,” Mach. Learn., vol. 8, no. 3–4, pp. 279–292, 1992.
11. van Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with double Q-learning,” in Proc. 30th AAAI Conf. Artif. Intell., 2016,
pp. 2094–2100.
12. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas, “Dueling network architectures for deep reinforcement
learning,” in Proc. 33rd Int. Conf. Mach. Learn. (ICML), 2016, pp. 1995–2003.
13. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv:1707.06347, 2017.
14. T. Schaul, J. Quan, I. Antonoglou, and D. Silver, “Prioritized experience replay,” in Proc. Int. Conf. Learn. Represent. (ICLR), 2016.
15. Mao, M. Alizadeh, I. Menache, and S. Kandula, “Resource management with deep reinforcement learning,” in Proc. 15th ACM Workshop
Hot Topics Netw. (HotNets), 2016, pp. 50–56.
16. Qiu, S. S. Banerjee, S. Jha, Z. T. Kalbarczyk, and R. K. Iyer, “FIRM: An intelligent fine-grained resource management framework for SLOoriented
microservices,” in Proc. 14th USENIX Symp. Oper. Syst. Design Implement. (OSDI), 2020, pp. 805–825.
17. Tan et al., “iBTune: Individualized buffer tuning for large-scale cloud databases,” Proc. VLDB Endow., vol. 12, no. 10, pp. 1221–1234,
2019.
18. Ma, D. Van Aken, A. Hefny, G. Mezerhane, A. Pavlo, and G. J. Gordon, “Query-based workload forecasting for self-driving database
management systems,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2018, pp. 631–645.
19. Sharma, F. M. Schuhknecht, and J. Dittrich, “The case for automatic database administration using deep reinforcement learning,”
arXiv:1801.05643, 2018.
20. Kossmann, A. Kastius, and R. Schlosser, “SWIRL: Selection of workload-aware indexes using reinforcement learning,” in Proc. 25th Int.
Conf. Extending Database Technol. (EDBT), 2022, pp. 155–168.
21. R. Marcus et al., “Neo: A learned query optimizer,” Proc. VLDB Endow., vol. 12, no. 11, pp. 1705–1718, 2019.
22. R. Marcus and O. Papaemmanouil, “Deep reinforcement learning for join order enumeration,” in Proc. 1st Int. Workshop Exploiting Artif.
Intell. Techn. Data Manage. (aiDM), 2018, pp. 1–4.
23. S. Krishnan, Z. Yang, K. Goldberg, J. Hellerstein, and I. Stoica, “Learning to optimize join queries with deep reinforcement learning,”
arXiv:1808.03196, 2018.
24. T. Kraska, A. Beutel, E. H. Chi, J. Dean, and N. Polyzotis, “The case for learned index structures,” in Proc. ACM SIGMOD Int. Conf.
Manage. Data, 2018, pp. 489–504.
25. T. Kraska et al., “SageDB: A learned database system,” in Proc. 9th Biennial Conf. Innovative Data Syst. Res. (CIDR), 2019.
26. Alshiekh, R. Bloem, R. Ehlers, B. Könighofer, S. Niekum, and U. Topcu, “Safe reinforcement learning via shielding,” in Proc. 32nd AAAI
Conf. Artif. Intell., 2018, pp. 2669–2678.
27. García and F. Fernández, “A comprehensive survey on safe reinforcement learning,” J. Mach. Learn. Res., vol. 16, pp. 1437 –1480, 2015.
28. Dulac-Arnold et al., “Challenges of real-world reinforcement learning: Definitions, benchmarks and analysis,” Mach. Learn., vol. 110, no.
9, pp. 2419–2468, 2021.
29. Y. Sakasegawa, “An approximation formula Lq ≃ αρβ/(1 − ρ),” Ann. Inst. Stat. Math., vol. 29, no. 1, pp. 67–75, 1977.
30. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. Int. Conf. Learn. Represent. (ICLR), 2015.
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