Machine Learning Algorithms for Predictive Database Capacity Planning and Resource Management
DOI:
https://doi.org/10.36676/dira.v12.i2.149Keywords:
Machine Learning Algorithms, ARCH-HEROAbstract
In this work, the author examines the utilization of ready algorithmic tools of machine learning in the context of database medium and its capacity forecasting issues. They point to the growing need for ARCH-HERO scalable database systems that can easily adapt to changes in workload to maximize the usage of available resources. Studied simulation models only working with AI have illustrated the effectiveness of machine learning techniques for improving the functioning of a DBMS. Real-time case studies here expound on how ML models forecast database capacity needs, keep operational costs low, and enhance system efficiency. Other issues that concern DBMS and/or applying ML include challenges and prospects: the complexity of integration and/or embedding of ML into DBMS is presented along with potential solutions. This paper aims to share perspectives on the subsequent evolution of the databases utilized in ML and their ability to improve the resource management process.
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