Machine Learning-Driving Optimization of Legacy Database Systems

Authors: Bhanu Prakash Reddy Rella

DOI: https://doi.org/10.37082/IJIRMPS.v10.i6.232231

Short DOI: https://doi.org/g87djv

Country: USA

Full-text Research PDF File:   View   |   Download

Abstract: The emergence of machine learning (ML) as a solution to enhance the database management system and operational performance is becoming one of the trends in the data management. Legacy systems (quite often suffer from) poor performance. These issues prevent business institutions from growing and keep up with an increasingly information-driven climate. By utilizing the latest ML technologies, companies can replace old systems, keep up with today's requirements, and achieve overall performance. This paper will be about using machine learning in the old database systems optimization, particularly for query performance, the resources, and the predictive maintenance strategies, optimizing.
The first topic is query optimization, which has historically been challenging with traditional methods in handling dynamic workloads. Machine learning algorithms mine the history of query running data to identify the pattern and forecast the execution times for a more efficient query execution plan. Things like reinforcement learning permit an adaptive optimization approach to a real-time performance metric. Introducing those ML-based methods leads to a quicker query response time and results in a more significant customer satisfaction through faster, stable data access. This forward-looking optimization is essential to empower organizations to interact in advancing user requirements and arrange efficiently with more significant information.
Another important aspect is resource management, which may be leveraged much by machine learning to allocate computing resources in legacy systems. Using forecasting models, businesses can anticipate the workload in heads and stress some resources before high demand periods. This approach of day-to-day thinking of repair performance whilst maximizing the use of resources results in cost saving on financial and enhanced system efficiency. In addition to predictive maintenance by machine learning, it supports the transition from reactive maintenance methods to proactive, minimizes downtime, and achieves better system reliability. By preventing potential future issues, businesses can still enjoy high service availability, keep their legacy systems operating with high performance, and efficiently serve business-critical operations. Generally, the application of machine learning in matured database systems is a golden opportunity for organizations to augment their data management capabilities and to keep pace with the rapidly building landscape.

Keywords: Machine Learning, Legacy Systems, Database Optimization, Performance Tuning, Query Execution, Predictive Maintenance, Data Analysis, Reinforcement Learning, Anomaly Detection, Index Creation, Caching Strategies, Data Retrieval, Resource Allocation, System Degradation, Operational Efficiency, Business Intelligence, Data-Driven Decisions, Automation, Scalability, Downtime Minimization, Cost Savings, Historical Data, Real-Time Insights, System Reliability, Architectural Transition, ML Algorithms, Efficiency Improvement, Legacy Database Integration, Critical Failures, Organizational Competitiveness

Paper Id: 232231

Published On: 2022-12-08

Published In: Volume 10, Issue 6, November-December 2022

Share this