CLOUD BASED AI SYSTEM FOR PREDICTING CLOUD RESOURCE DEMAND [AUTO-SCALING]

The Cloud-Based AI System for Predicting Cloud Resource Demand (Auto Scaling) is an intelligent web-based solution designed to optimize cloud infrastructure by forecasting resource requirements dynamically. This system leverages machine learning techniques to analyze historical usage patterns, real-time system metrics, and workload trends to accurately predict future demand for computing resources such as CPU, memory, and storage. By integrating predictive analytics with cloud platforms, the system enables automated scaling decisions, ensuring efficient resource allocation while minimizing operational costs and preventing system overloads.The application is developed using Flask for backend processing and integration, along with HTML/CSS for creating an interactive and user-friendly frontend interface. Real-time data simulation and monitoring dashboards provide insights into system performance, while alert mechanisms notify users of unusual demand patterns. By incorporating AI-driven forecasting and automation, this system enhances cloud performance, improves scalability, and ensures high availability of services.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-28
DOI
https://doi.org/10.5281/zenodo.19841867
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
0.00
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article

CLOUD BASED AI SYSTEM FOR PREDICTING CLOUD RESOURCE DEMAND [AUTO-SCALING]

Prasanna G, Narmatha M
Zenodo (CERN European Organization for Nuclear Research)
Cloud Computing and Resource Management
article

CLOUD BASED AI SYSTEM FOR PREDICTING CLOUD RESOURCE DEMAND [AUTO-SCALING]

Prasanna G, Narmatha M
article en

Abstract

The Cloud-Based AI System for Predicting Cloud Resource Demand (Auto Scaling) is an intelligent web-based solution designed to optimize cloud infrastructure by forecasting resource requirements dynamically. This system leverages machine learning techniques to analyze historical usage patterns, real-time system metrics, and workload trends to accurately predict future demand for computing resources such as CPU, memory, and storage. By integrating predictive analytics with cloud platforms, the system enables automated scaling decisions, ensuring efficient resource allocation while minimizing operational costs and preventing system overloads.The application is developed using Flask for backend processing and integration, along with HTML/CSS for creating an interactive and user-friendly frontend interface. Real-time data simulation and monitoring dashboards provide insights into system performance, while alert mechanisms notify users of unusual demand patterns. By incorporating AI-driven forecasting and automation, this system enhances cloud performance, improves scalability, and ensures high availability of services.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 2%
Cloud Computing and Resource Management
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