PREDICTIVE OPTIMIZATION THROUGH DEEP LEARNING: A METHODOLOGICAL FRAMEWORK FOR REAL-TIME RESOURCE ALLOCATION

Abstract

Efficient allocation of resources in dynamic environments requires predictive and adaptive methodologies beyond rule-based or reactive strategies. This study proposes a methodological framework that leverages deep learning to optimize resource allocation in real time. Drawing on time-series forecasting models, the framework integrates predictive modelling with adaptive decision-making to allocate resources proactively under fluctuating demand. Using real-time metrics as input signals, the deep learning model anticipates future requirements and informs allocation strategies, thereby reducing latency and improving utilization efficiency. To demonstrate its applicability, the framework is implemented within a large-scale distributed system, where results indicate a significant improvement in prediction accuracy, system responsiveness, and overall resource efficiency compared with threshold-based methods. Beyond the technical application, the framework contributes methodologically by illustrating how predictive optimization through deep learning can serve as a generalizable approach to decision-making under constraints in complex, real-time settings.