Selective High-Frequency Database Backups Based on Predicted Events
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data backup systems often perform remote backups less frequently than ideal due to resource constraints, leading to potential data loss during events like natural disasters or outages, without efficiently adjusting backup frequencies based on predicted events.
Innovation Solution
A configuration system dynamically determines backup frequencies by analyzing historical data and third-party data, such as weather or geographic event data, to increase or decrease backup frequency based on predicted likelihoods of data loss or change, using machine learning models to optimize backup schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If backup frequency is increased to reduce data loss, then reliability is improved, but network usage and resource consumption increase
Solution Approach 1:
The backup frequency is made dynamic rather than static. The system continuously monitors predicted event likelihoods and adjusts backup frequencies accordingly - increasing frequency when risks are high and decreasing frequency when risks are low, thereby optimizing the balance between reliability and resource consumption
Solution Approach 2:
The system changes the backup frequency parameter based on predicted event likelihoods. By adjusting this critical parameter dynamically, the system can allocate network resources more efficiently while maintaining adequate backup reliability for high-risk periods
2Reliability
If backup frequency is increased to reduce data loss, then reliability is improved, but device complexity increases
Solution Approach 1:
The system introduces an intermediary layer that uses predicted event likelihoods as a mediator between the backup system and external events. This intermediary mechanism simplifies the overall system architecture by providing a clear decision criterion for adjusting backup frequencies based on forecasted conditions
3Device complexity
If static backup frequency is used, then device complexity is reduced, but reliability deteriorates during predicted events
Solution Approach 1:
The system performs preliminary actions by adjusting backup frequencies in advance based on predicted event likelihoods. By proactively increasing backup frequency before predicted events occur, the system prepares the backup infrastructure ahead of time without requiring complex real-time responses during actual events
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selectively creating high frequency data backups. One of the methods includes maintaining configuration data that indicates a backup frequency at which backups are scheduled to be made for a database, and third party data that identifies one or more predicted events in a geographic area in which the database is physically located; determining, using the third party data, whether a predicted likelihood that the database will experience data loss during a future time period satisfies a threshold likelihood; in response to determining whether the predicted likelihood satisfies the threshold likelihood, selectively changing the backup frequency in the configuration data to be a second, different value that is different than a first value or determining to skip updating the backup frequency; and initiating, using the backup frequency, a backup of at least a second portion of the database.