Backup Scheduling via Predicted Duration and Buffer Periods
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Solution Overview
Problem
Current backup systems face challenges in meeting user-defined recovery point objectives (RPOs) due to varying backup strategies and resource utilization inefficiencies, leading to potential failures during peak usage periods and resource wastage.
Innovation Solution
A method that determines buffer periods and predicted execution durations for pending backup operations, prioritizes them based on these factors, and executes them accordingly, ensuring that backup operations meet user requirements without increasing computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If backup operations are executed according to fixed scheduling without prioritization, then resource allocation is simple, but backup operations may fail to meet user-defined RPOs during peak usage periods
Solution Approach 1:
The system performs preliminary actions by determining buffer periods and predicted execution durations for pending backup operations before actual execution. This advance preparation enables prioritization that ensures RPO compliance during peak usage periods without increasing computing resources.
Solution Approach 2:
The invention introduces dynamic prioritization of backup operations based on predicted execution durations and buffer periods. Instead of fixed scheduling, the system adapts the execution order of backup operations dynamically, adjusting priorities according to current system state and predictions, thereby improving RPO compliance while managing resource constraints.
2Productivity
If more computing resources are allocated to backup operations, then more backup operations can be completed, but resource cost increases
Solution Approach 1:
The system implements self-service by using historical execution durations to predict future backup operation durations. This self-prediction capability allows the system to optimize its own scheduling and resource allocation without external intervention or additional computing resources, thereby maintaining productivity while avoiding increased resource consumption.
Solution Approach 2:
The invention changes parameters by introducing predicted execution durations and buffer periods as new scheduling parameters. These parameter changes enable more efficient resource utilization and backup throughput optimization without requiring additional computing resources, as the system leverages historical data to make informed scheduling decisions.
3Loss of energy
If backup operations are executed in parallel without prioritization, then resource utilization is inefficient, but implementation is simple
Solution Approach 1:
The system determines predicted execution durations and buffer periods as preliminary actions before executing backup operations in parallel. This preliminary analysis enables intelligent prioritization that reduces resource wastage by ensuring that parallel execution is based on informed decisions about which operations should take precedence, rather than simple unprioritized parallelism.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for backup. The method includes: determining a plurality of buffer periods associated with a plurality of pending backup operations of a plurality of objects, each buffer period being a preprocessing period for a corresponding pending backup operation. The method further includes: determining a plurality of predicted execution durations of the plurality of pending backup operations based on historical execution durations of respective historical backup operations of the plurality of objects. The method further includes: determining priorities of the plurality of pending backup operations based on the plurality of predicted execution durations and the plurality of buffer periods. The method further includes: executing the plurality of pending backup operations based on the priorities.