System for Optimizing Cloud Infrastructure Using Machine Learning

Abstract

The present invention discloses a system for optimizing cloud infrastructure using machine learning, aimed at enhancing performance, scalability, and cost efficiency. The system consists of a data collection module to gather real-time and historical metrics, a feature extraction module to identify patterns, a machine learning model to predict optimal resource configurations, and an optimization engine to dynamically adjust cloud settings based on these predictions. A feedback loop continuously refines the model, enabling adaptive optimization in real-time. The invention effectively manages dynamic workloads, minimizes costs, and supports multi-cloud environments, providing a comprehensive solution for cloud resource optimization.

Specification

Description:The present invention relates to cloud computing, specifically focusing on a system and method for optimizing cloud infrastructure. It employs machine learning techniques to dynamically adjust cloud resources, enhancing performance, scalability, and cost efficiency across various applications and workloads.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Cloud computing has revolutionized the way businesses operate by offering scalable, flexible, and on-demand access to computing resources. Enterprises leverage cloud services to host applications, manage databases, and process data without investing hea , Claims:1. A system for optimizing cloud infrastructure using machine learning, comprising:
 a data collection module configured to gather real-time and historical cloud resource metrics;
 a feature extraction module to process and extract features from the collected data;
 a machine learning model, trained on historical usage data, to predict optimal cloud resource configurations;
 an optimization engine that dynamically adjusts cloud infrastructure settings based on predictions from the machine learning model;
 a feedback loop that provides continuous monitoring and real-time adaptation of the machine learning model.
2. A method for optimizing cloud infrastructure using machine learning, comprising:
 collecting real-time and historical metrics of cloud resources;
 extracting features from the collected data to identify usage patterns;
 training a machine learning model on the extracted features to predict optimal configurations for cloud resources;
 adjusting cloud infrastructure settings based on the pred

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