Role of Machine Learning in Optimizing IT Infrastructure

Abstract

Modern societies depend heavily on infrastructure systems; however, these systems are very vulnerable to both natural and man-made calamities. Repair-scheduling techniques are necessary for effective post-disaster recovery, given the system's requirement to share restricted resources. The study emphasises the potential of a comprehensive strategy for telecommunications energy efficiency, which includes implementing intelligent power management systems, employing green data centres with cutting-edge power management techniques, deploying energy-efficient hardware, and optimising network traffic flow. In order to address these problems, we provide a unique strategy for optimising infrastructure systems' post-disaster recovery by using Deep Reinforcement Learning (DRL) techniques and integrating a specialised resilience indicator to guide the optimisation. A graph-based structure is used to describe the system topology, and a sequential decision-making problem is used to design the system's recovery procedure. In order to enhance model performance, the research uses techniques like random oversampling and undersampling on a dataset spanning from 2015 to 2022 that include parameters like pipe age, material, diameter, and maintenance history. In recall (0.795 vs. 0.683), a crucial indicator for managing water infrastructure, XGBoost performs better than logistic regression. XGBoost has better overall performance with a higher Matthew's correlation coefficient (MCC) and F1 score, successfully balancing accuracy and recall, even if logistic regression offers slightly greater precision (0.695). Because it tackles the need for reliable predictive models to foresee and lessen water pipeline breakdowns, this study is crucial. This research promotes more effective and sustainable water management for infrastructure by providing a thorough framework for handling massive datasets and demonstrating how precise forecasts may save maintenance expenses and water waste.