Backup Manager Load Balancing for Cyclic Workloads
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data protection systems face challenges in efficiently generating backup schedules that meet Recovery Point Objectives (RPOs) while balancing computational load across time, leading to potential data loss and system performance issues due to limited computational resources in backup storages.
Innovation Solution
A method that subdivides the backup scheduling process into phases to select optimization periodicity, distribute backup entities across time, and determine specific backup generation times, reducing computational complexity from exponential to linear and distributing the load to prevent system slowdowns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If backups are generated frequently to meet Recovery Point Objectives, then data protection reliability is improved, but computational load on backup storage increases causing system performance degradation
Solution Approach 1:
The patent segments the backup generation process into discrete optimization periods with specific periodicity. Backups are distributed across multiple time periods rather than concentrated, dividing the computational load into manageable segments that prevent system overload while maintaining data protection requirements.
Solution Approach 2:
The patent implements periodic backup generation based on determined optimization periodicity. Backups are generated at regular intervals defined by the optimization periodicity parameter, creating a rhythmic pattern of computational load that allows system resources to recover between backup operations, thus maintaining system performance while ensuring data protection.
2Productivity
If optimization periodicity is reduced to distribute backup load, then system performance is improved, but the complexity of scheduling increases
Solution Approach 1:
The patent determines optimization periodicity as a specific parameter that controls the timing and distribution of backups. By adjusting this parameter, the system can optimize between load distribution and scheduling complexity. The parameter-based approach provides a systematic way to manage scheduling without requiring complex algorithms.
Solution Approach 2:
The system automatically determines the optimization periodicity based on its own operational characteristics and workload patterns. This self-determination eliminates the need for external complex scheduling inputs, allowing the system to autonomously optimize its backup schedule while maintaining simplicity.
Data Source
AI summary
A backup orchestrator for providing backup services to entities includes storage for storing recovery point objectives for the entities and a backup manager. The backup manager selects an optimization periodicity based a number of backups to be generated to meet a portion of the recovery point objectives; makes a determination that at least one of the portion of the recovery point objectives has a maximum allowable unbacked up period of time that is greater than the optimization periodicity; in response to the determination: load balances the number of backups across multiple optimization periods, based on the optimization periodicity, of a balanced backup schedule; selects a backup generation time for each of the to be generated backups in each of the optimization periods of the balanced backup schedule; and generates the number of backups using the balanced backup schedule.