Adaptive Backup Scheduling for Recovery Data Objectives
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Solution Overview
Problem
Existing database backup systems face challenges in managing data loss during failures, as the amount of data lost varies significantly with usage patterns, making it difficult to balance recovery time objectives with data volume considerations.
Innovation Solution
Implementing adaptive Service-Level Agreements (SLAs) based on predictive analysis of data change rates in transaction logs, allowing for scheduled backups that minimize data loss within defined recovery data objectives (RDOs), thereby optimizing backup frequency and reducing system burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If backup frequency is increased to minimize data loss, then data loss is reduced, but system burden and resource consumption increase
Solution Approach 1:
The patent implements dynamic backup scheduling by continuously monitoring data change rates and adjusting backup frequency in real-time. The system transitions from static, fixed-interval backups to adaptive backups that respond to actual data modification patterns, thereby minimizing data loss during high-activity periods while reducing system burden during low-activity periods.
Solution Approach 2:
The system changes the backup interval parameter based on observed data change rates. When the rate of data change exceeds a threshold, the backup frequency is increased; when the rate is low, the frequency is decreased. This parameter adaptation allows the system to optimize between data loss prevention and resource consumption dynamically.
2Reliability
If backup frequency is increased to ensure data recovery, then recovery data objective is met, but backup time and resource usage increase
Solution Approach 1:
The system performs preliminary analysis of data change patterns and predicts future change rates. Based on these predictions, it proactively schedules backups at optimal times before significant data changes occur, ensuring recovery objectives are met while avoiding unnecessary backups during periods of predicted low activity.
Solution Approach 2:
The system continuously monitors actual data change rates and compares them against predicted rates. This feedback loop allows the system to adjust future predictions and backup schedules, refining the timing of backups to precisely when they are needed most, thereby meeting recovery objectives with minimal backup time consumption.
3Ease of operation
If fixed interval backups are used, then scheduling is simple, but data loss varies significantly with usage patterns
Solution Approach 1:
The system automatically monitors its own data change patterns and self-adjusts backup schedules without external intervention. It services itself by detecting when data modification rates change and autonomously modifying backup frequency, maintaining both simplicity for the user and adaptability to usage patterns.
Data Source
AI summary
A method may include, obtaining, by one or more processors, transaction logs of a database server captured in a predetermined time period, identifying, by the one or more processors, a rate of change of data within the transaction logs, classifying, by the one or more processors, portions of the time period according to the rate of change of data within the portions of the time period, and setting, by the one or more processors, a service level agreement (SLA) for each portion of the time period according to the classification of each portion.