TY  - JOUR
AU  - N Murali Krishna
AU  - Vuggam Rishika
AU  - Ravuri Chandra Neelaveni
AU  - Devasani Rama Krishna
PY  - 2025
DA  - 2025/06/27
TI  - Cloud Resource Forecasting Using LSTM Neural Networks
JO  - Global Journal of Engineering Innovations and Interdisciplinary Research
VL  - 5
IS  - 4
AB  - 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.
SN  - 3066-1226
UR  - https://dx.doi.org/10.33425/3066-1226.1132
DO  - 10.33425/3066-1226.1132