Predictive Backup Scheduling for Data Protection
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Solution Overview
Problem
Existing backup systems fail to manage overlapping backup jobs effectively, leading to data loss when a scheduled backup is skipped due to the completion of a previous backup job taking longer than expected, resulting in missed backup schedules and potential data corruption.
Innovation Solution
Implementing a predictive scheduled backup process that dynamically estimates the completion time of ongoing backup jobs and reschedules subsequent jobs to ensure continuous data protection without skipping scheduled backups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If backup jobs are scheduled on a regular period, then backup frequency is maintained, but data loss occurs when backup jobs overlap and are skipped
Solution Approach 1:
The system performs preliminary actions by detecting overlapping backup jobs before they execute and proactively rescheduling them. The backup system monitors scheduled backup jobs, identifies potential overlaps with ongoing jobs, and adjusts start times in advance to prevent data loss from skipped backups.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring the status of ongoing backup jobs and using this information to dynamically adjust the scheduling of subsequent backup jobs. When an overlap is detected, the system receives feedback about the ongoing job's progress and modifies the schedule accordingly to ensure all backups execute successfully.
2Reliability
If a next scheduled backup job waits for the current backup job to complete, then data integrity is maintained, but backup frequency decreases and productivity drops
Solution Approach 1:
The backup scheduling system transitions from a static, fixed schedule to a dynamic schedule that adapts in real-time. The system continuously monitors ongoing backup jobs and dynamically adjusts the start times of subsequent backup jobs based on actual completion times, ensuring both data integrity and optimal backup frequency.
Solution Approach 2:
The system changes the timing parameter of backup jobs dynamically based on the state of ongoing backups. Instead of using fixed time intervals, the system adjusts the start time parameter of each backup job based on when previous jobs actually complete, maintaining data integrity while preserving backup frequency.
3Productivity
If backup jobs are executed without overlap detection, then system resources are utilized efficiently, but scheduled backups are skipped resulting in data loss
Solution Approach 1:
The system introduces an intermediary scheduling layer that mediates between resource utilization goals and data protection requirements. This intermediary component detects potential overlaps and coordinates backup job execution to prevent data loss while minimizing impact on resource utilization by making intelligent scheduling decisions.
Data Source
AI summary
Embodiments for predictive scheduling of backups in a data protection system by initiating a first backup job in a series of scheduled consecutive backup jobs, wherein a second backup job is allowed to begin only after the first backup job is finished and not active, detecting whether or not the first backup job is still active when a second job is to start, and if so, estimating an amount of additional time required to finish the first backup job. The second backup job is then rescheduled to start at least at the end of the additional time. The estimated amount of additional time is determined using a throughput to target storage device parameter. This parameter is periodically checked to determine if there is a change to the estimated amount of additional time, and if so, the estimated time is recalculated based on the changed parameter.