Intelligent Backup Scheduling for Predictive Storage Management

Abstract: Data is a central part of everyday digital work. Students, developers, companies, and researchers keep documents, source code, project files, reports, images, databases, and other valuable information on their computers. Such data can be lost through accidental deletion, hard-disk failure, software crashes, malware, power failure, or file corruption, which makes regular backups necessary. Many conventional backup systems follow a fixed schedule, such as a backup every day or every week. This approach is easy to use, but it ignores whether the files have actually changed. A backup may be created even when nothing important has changed, which wastes time, storage space, and system resources. Likewise, if a large amount of data changes right after a scheduled run, the new data stays unprotected until the next one. This project proposes an adaptive backup scheduling system developed in Python. The system monitors a selected source folder and compares its current files with a previous snapshot. New, modified, and deleted files are identified from the file path, file size, and last-modified time. A backup is triggered only when the amount of changed data reaches a configured threshold or when too much time has passed since the last successful backup. CPU usage and free space are also checked before a backup begins. The current prototype uses incremental backup, so only new or modified files are copied to the destination. Each backup is saved in a separate timestamped folder, and a retention policy removes older folders beyond a fixed limit. The project also includes modules for logging, feature generation, training-data generation, and predictive storage analysis, which form a base for future machine-learning integration. The proposed system aims to make backups more efficient, reliable, and responsive to real changes in data.

Keywords: Adaptive backup, incremental backup, Python, file snapshot, storage monitoring, backup scheduling, predictive storage management.