Reducing Latency and Storage Costs in Cloud Applications Through Advanced Data Management

Junk files, including outdated backups, redundant document versions, and orphaned objects, accumulate in cloud storage, leading to inefficiencies in data retrieval, increased latency, and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.