Journal Press India^®

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Efficient server resource management is critical for optimal performance in cloud-based systems. This work introduces a Proactive Server Resource Management Application employing AI and Deep Learning, specifically Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models. These models analyze historical data, predict resource utilization, and optimize allocation, addressing challenges like vanishing gradients through LSTM's memory cells and gating mechanisms. The application offers insights into trends, patterns, and potential issues, enabling proactive interventions for optimal resource allocation, anomaly detection, and performance mitigation. Leveraging RNN and LSTM models, the project enhances understanding of server resource consumption patterns, facilitating informed decisions for organizational efficiency.

Keywords

Server resource consumption; Deep learning models; Recurrent Neural Network (RNN); Long Short-Term Memory (LSTM); Historical data analysis; Proactive server management; Temporal dependencies; Vanishing gradient problem; Variable-length sequences; Resource allocation; Trend analysis; Anomaly detection; Performance optimization

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