Authors :
Vamsi Kalyan Jupudi; Nanda Kishore Mysuru; Ritheesh Mekala
Volume/Issue :
Volume 9 - 2024, Issue 6 - June
Google Scholar :
https://tinyurl.com/5x77ztk5
Scribd :
https://tinyurl.com/jtpbthr7
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24JUN1908
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Traditional methods of performance tuning in
Database Management Systems (DBMS) are facing
significant challenges in adapting to the dynamic nature
of modern workloads. Reactive approaches and static
configurations often lead to performance bottlenecks and
inefficient resource utilization. In response, this paper
proposes a novel approach for workload-based
performance tuning through the integration of Artificial
Intelligence (AI). By leveraging AI techniques such as
machine learning and predictive modeling, the proposed
methodology aims to automate the analysis of workload
patterns, predict future trends, and dynamically adjust
DBMS configurations for optimal performance. The
paper discusses the key components of the proposed
methodology, including workload characterization,
predictive modeling, and adaptive configuration
management. A hypothetical case study in an e-commerce
database environment illustrates the implementation and
potential performance improvements achieved through
AI-powered tuning. Furthermore, the paper explores
real-world applications, future research directions,
challenges, and best practices for implementing
workload-based tuning with AI integration. Overall, this
paper presents a comprehensive framework for
leveraging AI to enhance DBMS performance, scalability,
and efficiency in dynamic environments.
Keywords :
Component, Formatting, Style, Styling, Insert (Key Words).
References :
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- Z. Yan, J. Lu, N. Chainani and C. Lin, "Workload-Aware Performance Tuning for Autonomous DBMSs," 2021 IEEE 37th International Conference on Data Engineering (ICDE), Chania, Greece, 2021
- M. Zhang, P. Martin, W. Powley and J. Chen, "Workload Management in Database Management System: A Taxonomy (Extended Abstract)," 2018 IEEE 34th International Conference on Data Engineering (ICDE), Paris, France, 2018
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- S. Singh, "A Neural Network based Attendance Monitoring and Database Management System using Fingerprint Recognition and Matching," 2019 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, 2019
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- A. Talun, P. Drozda, S. Yelmanov, Y. Romanyshyn and O. Tehlivets, "Convolutional Neural Network Assessment of Image Quality Based on the TID2013 Database," 2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Dortmund, Germany, 2023
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Traditional methods of performance tuning in
Database Management Systems (DBMS) are facing
significant challenges in adapting to the dynamic nature
of modern workloads. Reactive approaches and static
configurations often lead to performance bottlenecks and
inefficient resource utilization. In response, this paper
proposes a novel approach for workload-based
performance tuning through the integration of Artificial
Intelligence (AI). By leveraging AI techniques such as
machine learning and predictive modeling, the proposed
methodology aims to automate the analysis of workload
patterns, predict future trends, and dynamically adjust
DBMS configurations for optimal performance. The
paper discusses the key components of the proposed
methodology, including workload characterization,
predictive modeling, and adaptive configuration
management. A hypothetical case study in an e-commerce
database environment illustrates the implementation and
potential performance improvements achieved through
AI-powered tuning. Furthermore, the paper explores
real-world applications, future research directions,
challenges, and best practices for implementing
workload-based tuning with AI integration. Overall, this
paper presents a comprehensive framework for
leveraging AI to enhance DBMS performance, scalability,
and efficiency in dynamic environments.
Keywords :
Component, Formatting, Style, Styling, Insert (Key Words).