CLOUD-BASED DATA STORAGE OPTIMIZATION TECHNIQUES

Abstract

The present invention provides a cloud-based data storage optimization system designed to improve storage efficiency, reduce costs, and enhance data retrieval performance. The system utilizes a combination of predictive analytics, machine learning, tiered storage, dynamic data migration, and data optimization techniques such as compression and deduplication. Data is classified into multiple storage tiers based on access frequency and criticality, and storage resources are allocated dynamically based on real-time usage patterns and predictive models. The system automatically migrates data between tiers, compresses data to reduce storage footprint, and eliminates duplicate data to maximize storage efficiency. This innovative approach ensures cost-effective, high-performance storage management in cloud environments, optimizing both resource utilization and system performance while reducing operational costs.

Specification

Description:The embodiments of the present invention generally relate to cloud computing systems, particularly to methods and systems for optimizing data storage in cloud-based environments. The invention focuses on improving storage efficiency, reducing operational costs, and enhancing data retrieval performance in large-scale cloud data storage systems, leveraging advanced techniques such as machine learning, tiered storage, and dynamic resource allocation.

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.

With the increasing demand for cloud services, the volume of data being stored in cloud environments has rise , Claims:1. An optimized cloud-based data storage system, comprising:
a data categorization module configured to classify data into multiple storage tiers based on access frequency, data criticality, and storage needs, wherein the storage tiers include high-performance storage for frequently accessed data, mid-tier storage for moderately accessed data, and low-cost storage for infrequently accessed data;
a predictive analytics module configured to analyze historical data access patterns and predict future data storage requirements, thereby enabling proactive resource allocation;
a dynamic data migration module configured to automatically move data between storage tiers based on real-time usage patterns and predicted storage demands;
a compression module configured to compress stored data to reduce its storage footprint;
a deduplication module configured to identify and eliminate duplicate copies of data to optimize storage efficiency;
a load balancing module configured to distribute data across multiple cloud storage

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