Database Performance Optimization in Cloud Migrations: Case Study on Oracle and DB2 Automation
Keywords:
database optimization, cloud migration, Oracle automation, DB2 performanceAbstract
Performance optimization of database is a crucial challenge in large scale cloud migrations especially for enterprise systems depending on Oracle on Linux and DB2 on Windows. The aim of this study is to investigate automation driven strategies that enhance performance, mitigate bottlenecks, and ensure cost efficiency during cloud transitions. The key focus of this research are optimization techniques, automated patching mechanisms, parallel execution frameworks, and cloud-based performance tuning methodologies tailored for Oracle and DB2 environments.
Downloads
References
J. Dean and L. A. Barroso, "The Tail at Scale," Communications of the ACM, vol. 56, no. 2, pp. 74-80, 2013.
M. Stonebraker and U. Çetintemel, "One Size Fits All: An Idea Whose Time Has Come and Gone," in Proc. IEEE Int. Conf. Data Eng. (ICDE), Boston, MA, USA, 2005, pp. 2-11.
A. Pavlo et al., "Self-Driving Database Management Systems," in Proc. Conf. Innovative Data Syst. Res. (CIDR), Chaminade, CA, USA, 2017.
J. Duggan et al., "The BigDAWG Polystore System and Architecture," ACM Trans. Database Syst., vol. 42, no. 2, pp. 1-44, 2017.
S. Krishnan, H. Samet, and H. V. Jagadish, "Query Optimization Techniques for Large-Scale Distributed Databases," ACM Comput. Surv., vol. 51, no. 1, pp. 1-38, 2018.
Z. Wang et al., "AI4DB: Artificial Intelligence for Database Systems," arXiv preprint arXiv:2002.11276, 2020.
C. Binnig et al., "The End of Slow Networks: It’s Time for a Redesign," in Proc. Conf. Innovative Data Syst. Res. (CIDR), 2019.
J. Li et al., "Automatic Index Tuning: A Machine Learning Approach," in Proc. ACM SIGMOD Int. Conf. Manage. Data, Houston, TX, USA, 2018, pp. 33-50.
R. Marcus et al., "Neo: A Learned Query Optimizer," in Proc. Conf. Innovative Data Syst. Res. (CIDR), 2019.
T. Das et al., "Automatic Performance Tuning for Large-Scale Database Systems," IEEE Trans. Knowl. Data Eng., vol. 32, no. 9, pp. 1702-1716, 2020.
Y. Lu et al., "Query-based Workload Characterization for Autonomous Database Management," in Proc. ACM SIGMOD Int. Conf. Manage. Data, Beijing, China, 2021, pp. 12-25.
K. Zou et al., "AI-Driven Workload Prediction for Adaptive Database Management," in Proc. IEEE Int. Conf. Data Eng. (ICDE), 2020, pp. 245-256.
M. Boissier et al., "Deep Reinforcement Learning for Database Query Optimization," in Proc. ACM SIGMOD Int. Conf. Manage. Data, Amsterdam, Netherlands, 2019, pp. 19-30.
A. Abouzied et al., "Learning-Based Query Execution for Modern Database Systems," in Proc. IEEE Int. Conf. Big Data (BigData), 2020, pp. 1501-1512.
D. Park et al., "Database Knobs Optimization Using Bayesian Techniques," in Proc. Conf. Neural Inf. Process. Syst. (NeurIPS), 2019.
X. Pan et al., "Hyperparameter Optimization for Database Systems Using Reinforcement Learning," in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2021, pp. 66-78.
R. Kapoor et al., "Cloud-Native Database Performance: Challenges and Solutions," ACM Comput. Surv., vol. 55, no. 3, pp. 1-35, 2022.
H. Z. Yang et al., "Self-Optimizing Databases: Challenges and Future Directions," IEEE Trans. Knowl. Data Eng., vol. 34, no. 6, pp. 1230-1245, 2022.
M. S. Islam et al., "AI-Powered Database Query Optimization in Cloud Environments," in Proc. IEEE Int. Conf. Cloud Comput. (CLOUD), 2021, pp. 560-570.
P. Bonnet et al., "Database Architecture for AI-Powered Workload Orchestration," ACM Trans. Database Syst., vol. 47, no. 2, pp. 1-27, 2022.
