Backup Scheduling via Open File Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In information technology, scheduling backup jobs among multiple clients within a common backup window is challenging due to the presence of open files, which are often encountered during the backup process, especially in enterprise IT environments, leading to inefficiencies and additional manual backup requirements.
Innovation Solution
A method that collects historical data on open files during previous backup jobs, estimates the number of open files during the common backup window, infers the impact of skipping these files based on predetermined criteria, and combines this information with cost functions to schedule backup jobs in an optimal order, reducing the number of open files encountered during the backup process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If backup jobs are scheduled during a common backup window, then data protection is achieved, but the number of open files encountered increases, reducing backup reliability
Solution Approach 1:
The system performs preliminary actions by collecting historical information about file access patterns before the backup window, estimating which files are likely to be open during the backup, and using these estimates to optimize the backup schedule. This advance preparation allows the system to plan around files that are expected to be open, thereby improving backup reliability.
Solution Approach 2:
The system incorporates feedback by using historical information from previous backup jobs about which files were open and which were successfully backed up. This feedback loop allows the system to continuously improve its estimates and scheduling decisions, reducing the impact of open files on backup reliability over time.
2Productivity
If multiple backup clients share a common backup window, then resource utilization is improved, but scheduling complexity increases due to conflicting file access patterns
Solution Approach 1:
The system changes parameters by estimating the number of files open during the backup window based on historical data and using these estimates as input to a cost function. By optimizing the schedule to minimize this cost function, the system automatically resolves scheduling conflicts without manual intervention, maintaining high resource utilization while managing complexity through mathematical optimization.
Solution Approach 2:
The scheduling system performs self-service by automatically analyzing historical information, estimating open file patterns, and generating optimized schedules without requiring manual configuration. The system serves itself by using its own historical data to improve future scheduling decisions, reducing the need for external scheduling complexity.
3Ease of operation
If backup jobs are scheduled without considering open file patterns, then scheduling simplicity is maintained, but the number of failed backup attempts increases
Solution Approach 1:
The system extracts only the essential information needed for scheduling by collecting historical data about file access patterns and using this extracted information to estimate open files during the backup window. This selective extraction maintains scheduling simplicity while improving success rates, as the system focuses on the most relevant historical patterns rather than analyzing all possible variables.
Data Source
AI summary
A method for scheduling one or more backup jobs among backup clients with a common backup window. The method includes collecting and recording historical information pertaining to the backup clients with the common backup window. An application then estimates the number of open files during the common backup window based on the historical information and infers an impact of skipping a backup of the open files during the common backup window, where the impact is inferred from the historical information according to one or more predetermined criteria. The estimated number of open files and the inferred impact of skipping the backup of the estimated number of open files during the common backup window are then combined according to a predetermined cost function. The order of the backup jobs for the backup clients during the common backup window is then scheduled to reduce the predetermined cost function.