Cloud Resource Forecasting Using LSTM Neural Networks

Efficiently managing cloud resources is critical for balancing performance, availability, and cost in modern cloud environments. Allocating too many resources leads to unnecessary expenses, while insufficient resources can cause performance issues, bottlenecks, and downtime. This paper aims to solve these challenges with Long-Short-Term Memory (LSTM) neural networks to forecast cloud resource usage. LSTMs excel at processing time-series data, making it ideal for predicting trends in cloud resource consumption such as CPU usage, memory allocation. The project involves collecting historical cloud usage data and refining it through preprocessing to ensure accuracy and quality. Using this data, a robust LSTM model is designed and trained to provide reliable predictions of future resource demands. With accurate forecasts, cloud service providers can optimize resource allocation and implement proactive scaling strategies. The model helps ensure resources are neither over-provisioned nor underprovisioned, reducing waste while maintaining the capacity to handle workload demands. This approach leads to significant cost savings, improved system performance, and seamless scalability. Dynamic autoscaling is another benefit of this solution, enabling real-time adjustments to resource allocation based on predicted demand. This ensures that performance remains stable during usage spikes while avoiding unnecessary operational costs. To evaluate the model’s accuracy, metrics like Mean-Absolute-Error (MAE) and Root-Mean Squared-Error (RMSE) are analysed, with results showing that the LSTM model performs better than traditional forecasting techniques. This paper highlights the transformative potential of machine learning in cloud management. By accurately predicting resource needs, it offers an innovative, cost-efficient, and scalable solution for modern cloud operations, empowering organizations to meet demand reliably while keeping costs under control.