Capacity Planning and Resource Utilization in Large-Scale IT Projects - Data-Driven Approach: A Survey
Author(s): Vaishali Nagpure
Publication #: 2412008
Date of Publication: 09.10.2023
Country: USA
Pages: 1-11
Published In: Volume 9 Issue 5 October-2023
DOI: https://doi.org/10.5281/zenodo.14281940
Abstract
Capacity planning and resource utilization are essential aspects of managing large-scale IT systems, particularly in cloud environments, e-commerce platforms, ride-sharing services, and industrial manufacturing systems. These processes are crucial for ensuring that IT resources are used efficiently, costs are minimized, and system performance is maintained under varying demand conditions. Traditional methods of resource allocation often fall short in addressing the dynamic and complex nature of modern IT environments, necessitating more adaptive, data-driven approaches.This survey explores the application of machine learning (ML), optimization techniques, and orchestration tools in the context of large-scale IT projects. It provides a comprehensive analysis of how data-driven methods, including predictive analytics and real-time monitoring, are used to forecast demand, optimize resource allocation, and enhance system efficiency. Use cases are examined from diverse domains, such as predicting server load in e-commerce platforms, optimizing driver allocation in ride-sharing services, minimizing energy consumption in manufacturing, and scaling resources in cloud environments.Key technologies such as TensorFlow for predictive modeling, Google OR-Tools for optimization, and Kubernetes for container orchestration are discussed. The survey includes real-world examples and detailed workflows, illustrating how machine learning models can be deployed for demand forecasting, resource allocation, and autoscaling in production environments. Furthermore, it presents advanced visualizations to demonstrate the insights gained from data, such as heatmaps for resource allocation mismatches and time-series plots for server load predictions.In addition to the theoretical underpinnings, this survey provides practical guidance for deploying these techniques using platforms like AWS, Kubernetes, and Prometheus. It also covers optimization techniques such as linear programming and dynamic programming, showcasing how these methods are applied to solve real-world resource management problems.The paper concludes by emphasizing the importance of continuous monitoring, evaluation, and feedback loops to refine capacity planning strategies over time. Finally, future directions are explored, focusing on emerging trends like edge computing, federated learning, and sustainability in IT resource management. This survey serves as a comprehensive guide for researchers and practitioners looking to enhance the scalability, efficiency, and sustainability of large-scale IT projects.
Keywords: Capacity Planning, Resource Utilization, Data-Driven Decision-Making, Optimization Algorithms, Scalable IT Infrastructure
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