Cloud Clean: Cloud Storage Space Sustainability using Data Life Cycle Guard Scheme

Abstract

Junk files, including outdated backups, redundant document versions, and orphaned objects, accumulate in cloud storage, leading to inefficiencies in data retrieval, increased latency, and higher storage costs. As cloud applications grow in scale, managing and optimizing storage resources becomes crucial for maintaining performance and reducing operational overhead. The problem of unnecessary files taking up valuable space is especially critical in cloud environments where efficient resource management is essential for smooth operations. This project proposes a solution to optimize cloud data management by integrating automated cleanup, structured data lifecycle management, and advanced deduplication techniques. Regex algorithms will drive the cleanup process, identifying and eliminating obsolete files regularly to ensure that only relevant data is stored. Additionally, the Data Life Cycle Guard Scheme provides a framework for managing data according to predefined compliance rules, improving overall data governance and integrity. These measures aim to streamline data processes and maintain the efficiency of cloud applications. Fuzzy Matching techniques will further enhance the deduplication process, improving accuracy in identifying and removing duplicate files, thus optimizing storage space. By automating the identification of unnecessary files and improving data lifecycle management, this system helps reduce storage costs, minimize latency, and ensure that cloud applications run more efficiently. The solution is designed to set new standards in cloud data management, optimizing resource utilization and ensuring long-term sustainability for cloud-based environments

Introduction

Enterprise Cloud Overview:
An enterprise cloud integrates private, public, and distributed clouds into a unified IT environment with centralized control. It enables seamless management of applications and infrastructure across any cloud, improving performance, cost-efficiency, and compliance through virtualized IT resources.

Existing System Challenges:
Traditional systems handle junk files, backups, and orphaned objects manually through scheduled maintenance, backup rotation, and file auditing. This approach is time-consuming, error-prone, inefficient, lacks deduplication, is hard to scale, and poses security risks.

Proposed System - CloudClean:
CloudClean automates cloud data management to reduce storage clutter and costs while ensuring data integrity using:

• Automated Cleanup: Regex algorithms identify and remove obsolete files regularly.

• Data Life Cycle Guard: Structured policies govern data creation, storage, usage, and deletion to ensure compliance.

• Deduplication: Fuzzy matching algorithms detect and eliminate duplicate data during uploads to optimize storage.

This system enhances resource use, performance, and compliance, delivering efficient cloud data management.

System Modules:

1. Cloud Service Provider Web App:
   A user interface built with Python, Flask, MySQL, and frontend tools allowing provisioning, management, monitoring, security control, billing, and APIs for cloud resources.

2. Cloud User Interface:

   • Data Owner: Manages data, sets access controls, expiry dates, views reports, receives alerts, and handles billing.

   • Data User: Accesses shared data with permission.

3. Data Access Module:
   Supports secure uploading, accessing, and downloading of data with authentication and data integrity checks.

4. Data Life Cycle Guard:
   Enforces policies for data lifecycle management, retention, deletion, auditing, and compliance.

5. Data Deduplication Module:
   Uses fuzzy matching to detect and remove duplicates during upload, handling variations and logging actions.

6. Automated Cleanup Module:
   Applies Regex patterns to identify and remove junk or temporary files, ensuring a clean cloud storage environment.

7. Notification Module:
   Sends customizable alerts via multiple channels about critical system events, usage, and security, with logging for audit trails.

8. Logging and Reporting Module:
   Records system activities, enables real-time monitoring, auditing, and generates compliance reports for transparency and security.

Overall, the proposed CloudClean system provides a comprehensive, automated approach to managing cloud data efficiently, reducing costs, improving compliance, and enhancing operational performance.

Conclusion

In conclusion, the project represents a significant advancement in cloud data management, addressing the limitations of traditional systems and offering a comprehensive solution to optimize storage efficiency, reduce costs, and ensure data integrity. Through the implementation of automated cleanup processes, robust data lifecycle management, and efficient deduplication strategies, it streamlines data management workflows and mitigates the challenges associated with junk files, outdated backups, and orphaned objects. By leveraging automation tools and algorithms, this project simplifies the process of identifying and removing unnecessary files, ensuring that cloud storage remains clutter-free and optimized for performance. The integration of a structured data lifecycle management framework facilitates adherence to compliance standards and regulatory requirements, while also enabling efficient resource allocation and data retention practices. Furthermore, the incorporation of deduplication algorithms enhances storage efficiency by identifying and eliminating redundant data, thereby reducing storage costs and optimizing resource utilization. This not only improves the overall performance of cloud applications but also contributes to environmental sustainability by minimizing the carbon footprint associated with excess data storage. Additionally, it offers advanced monitoring and reporting capabilities, allowing users to track storage usage, data access patterns, and compliance metrics in real-time. This proactive approach to data management enables organizations to identify potential issues early and take corrective actions to maintain data integrity and security. In summary, the project represents a paradigm shift in cloud data management, offering a holistic approach to address the complexities and challenges of modern data environments. By combining automation, intelligent algorithms, and proactive monitoring, it empowers organizations to optimize their cloud storage resources, improve operational efficiency, and drive innovation in the digital age.

References

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Copyright

Copyright © 2025 Janakiraman S, Narmadha K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.