PERFORMANCE OPTIMIZATION TECHNIQUES IN DATABASE RELIABILITY ENGINEERING
Keywords:
Database Optimization, Machine Learning Integration, Performance Management, Automated Tuning, Multi-tenant ArchitectureAbstract
Database Reliability Engineering has evolved significantly in response to the growing complexity of enterprise data management systems. This article examines the transformative impact of modern optimization techniques, focusing on indexing strategies, caching architectures, and machine learning integration in database performance management. The article explores how automated tuning solutions and real-time monitoring systems have revolutionized database optimization, particularly in multi-tenant environments. Through analysis of contemporary best practices and implementation methodologies, this article demonstrates the effectiveness of systematic performance testing, validation frameworks, and documentation strategies in enhancing operational efficiency. The article highlights the crucial role of machine learning in predictive optimization and the importance of comprehensive testing protocols in maintaining system stability and performance.
References
Kwanele Ngcobo et al., "Enterprise Data Management Types, Sources and Real-Time Applications to Enhance Business Performance - A Systematic Review," International Journal of Data Management, September 2024. Available: https://www.researchgate.net/publication/384355238_Enterprise_Data_Management_Types_Sources_and_Real-Time_Applications_to_Enhance_Business_Performance_-_A_Systematic_Review
Hai Lue Lin et al., "Performance Management for Multi-Tenant Web Applications," IEEE Transactions on Cloud Computing, October 2010. Available: https://www.researchgate.net/publication/270044267_Performance_Management_for_Multi-Tenant_Web_Applications
Syed Nisar Hussain Bukhari, "Study and Evaluation of caching mechanisms in web applications," Department of Computer Science, University of Kashmir, 2024. Available: https://www.nielit.gov.in/sites/default/files/CPT-17_Computer%20Science_Syed%20Nisar.pdf
Sai Tarun Kaniganti, "Optimizing Database Performance for High-Load Systems," International Journal of Database Management, December 2022. Available: https://www.researchgate.net/publication/382017833_Optimizing_Database_Performance_for_High-Load_Systems
Bogdan Milisevic et al., "A Systematic Review of Deep Learning Applications in Database Query Execution," International Journal of Database Management, December 2024. Available: https://www.researchgate.net/publication/387179144_A_systematic_review_of_deep_learning_applications_in_database_query_execution
Raffaele Pugliese et al., "Machine Learning Applications in Modern Database Systems: A Comprehensive Review," Journal of Systems and Software, vol. 182, pp. 111124, December 2021. Available: https://www.sciencedirect.com/science/article/pii/S2666764921000485
Deepti Barhate et al., "A systematic review of machine learning and deep learning approaches in plant species detection," Journal of Data Management Systems, December 2024. Available: https://www.sciencedirect.com/science/article/pii/S2772375524002107
Raffaele Pugliese et al., "Machine learning-based approach: global trends, research directions, and regulatory standpoints," Journal of Systems and Software, vol. 182, pp. 111124, December 2021. Available: https://www.sciencedirect.com/science/article/pii/S2666764921000485
Maxime Gobert, et al., "Best practices of testing database manipulation code," Journal of Systems and Software, January 2023. Available: https://www.sciencedirect.com/science/article/pii/S0306437922000886
Ebiesuwa Seun et al., "Impact of Information Systems on Operational Efficiency: A Comprehensive Analysis," International Journal of Information Management, August 2023. Available: https://www.researchgate.net/publication/373267444_Impact_of_Information_Systems_on_Operational_Efficiency_A_Comprehensive_Analysis
